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Image processing is manipulation of an image that has been digitised and uploaded into a computer. Software programs modify the image to make it more useful, and can for example be used to enable image recognition.

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Specialty grand challenge article, grand challenges in image processing.

• Université Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des signaux et Systèmes, Gif-sur-Yvette, France

Introduction

The field of image processing has been the subject of intensive research and development activities for several decades. This broad area encompasses topics such as image/video processing, image/video analysis, image/video communications, image/video sensing, modeling and representation, computational imaging, electronic imaging, information forensics and security, 3D imaging, medical imaging, and machine learning applied to these respective topics. Hereafter, we will consider both image and video content (i.e. sequence of images), and more generally all forms of visual information.

Rapid technological advances, especially in terms of computing power and network transmission bandwidth, have resulted in many remarkable and successful applications. Nowadays, images are ubiquitous in our daily life. Entertainment is one class of applications that has greatly benefited, including digital TV (e.g., broadcast, cable, and satellite TV), Internet video streaming, digital cinema, and video games. Beyond entertainment, imaging technologies are central in many other applications, including digital photography, video conferencing, video monitoring and surveillance, satellite imaging, but also in more distant domains such as healthcare and medicine, distance learning, digital archiving, cultural heritage or the automotive industry.

In this paper, we highlight a few research grand challenges for future imaging and video systems, in order to achieve breakthroughs to meet the growing expectations of end users. Given the vastness of the field, this list is by no means exhaustive.

A Brief Historical Perspective

We first briefly discuss a few key milestones in the field of image processing. Key inventions in the development of photography and motion pictures can be traced to the 19th century. The earliest surviving photograph of a real-world scene was made by Nicéphore Niépce in 1827 ( Hirsch, 1999 ). The Lumière brothers made the first cinematographic film in 1895, with a public screening the same year ( Lumiere, 1996 ). After decades of remarkable developments, the second half of the 20th century saw the emergence of new technologies launching the digital revolution. While the first prototype digital camera using a Charge-Coupled Device (CCD) was demonstrated in 1975, the first commercial consumer digital cameras started appearing in the early 1990s. These digital cameras quickly surpassed cameras using films and the digital revolution in the field of imaging was underway. As a key consequence, the digital process enabled computational imaging, in other words the use of sophisticated processing algorithms in order to produce high quality images.

In 1992, the Joint Photographic Experts Group (JPEG) released the JPEG standard for still image coding ( Wallace, 1992 ). In parallel, in 1993, the Moving Picture Experts Group (MPEG) published its first standard for coding of moving pictures and associated audio, MPEG-1 ( Le Gall, 1991 ), and a few years later MPEG-2 ( Haskell et al., 1996 ). By guaranteeing interoperability, these standards have been essential in many successful applications and services, for both the consumer and business markets. In particular, it is remarkable that, almost 30 years later, JPEG remains the dominant format for still images and photographs.

In the late 2000s and early 2010s, we could observe a paradigm shift with the appearance of smartphones integrating a camera. Thanks to advances in computational photography, these new smartphones soon became capable of rivaling the quality of consumer digital cameras at the time. Moreover, these smartphones were also capable of acquiring video sequences. Almost concurrently, another key evolution was the development of high bandwidth networks. In particular, the launch of 4G wireless services circa 2010 enabled users to quickly and efficiently exchange multimedia content. From this point, most of us are carrying a camera, anywhere and anytime, allowing to capture images and videos at will and to seamlessly exchange them with our contacts.

As a direct consequence of the above developments, we are currently observing a boom in the usage of multimedia content. It is estimated that today 3.2 billion images are shared each day on social media platforms, and 300 h of video are uploaded every minute on YouTube 1 . In a 2019 report, Cisco estimated that video content represented 75% of all Internet traffic in 2017, and this share is forecasted to grow to 82% in 2022 ( Cisco, 2019 ). While Internet video streaming and Over-The-Top (OTT) media services account for a significant bulk of this traffic, other applications are also expected to see significant increases, including video surveillance and Virtual Reality (VR)/Augmented Reality (AR).

Hyper-Realistic and Immersive Imaging

A major direction and key driver to research and development activities over the years has been the objective to deliver an ever-improving image quality and user experience.

For instance, in the realm of video, we have observed constantly increasing spatial and temporal resolutions, with the emergence nowadays of Ultra High Definition (UHD). Another aim has been to provide a sense of the depth in the scene. For this purpose, various 3D video representations have been explored, including stereoscopic 3D and multi-view ( Dufaux et al., 2013 ).

In this context, the ultimate goal is to be able to faithfully represent the physical world and to deliver an immersive and perceptually hyperrealist experience. For this purpose, we discuss hereafter some emerging innovations. These developments are also very relevant in VR and AR applications ( Slater, 2014 ). Finally, while this paper is only focusing on the visual information processing aspects, it is obvious that emerging display technologies ( Masia et al., 2013 ) and audio also plays key roles in many application scenarios.

Light Fields, Point Clouds, Volumetric Imaging

In order to wholly represent a scene, the light information coming from all the directions has to be represented. For this purpose, the 7D plenoptic function is a key concept ( Adelson and Bergen, 1991 ), although it is unmanageable in practice.

By introducing additional constraints, the light field representation collects radiance from rays in all directions. Therefore, it contains a much richer information, when compared to traditional 2D imaging that captures a 2D projection of the light in the scene integrating the angular domain. For instance, this allows post-capture processing such as refocusing and changing the viewpoint. However, it also entails several technical challenges, in terms of acquisition and calibration, as well as computational image processing steps including depth estimation, super-resolution, compression and image synthesis ( Ihrke et al., 2016 ; Wu et al., 2017 ). The resolution trade-off between spatial and angular resolutions is a fundamental issue. With a significant fraction of the earlier work focusing on static light fields, it is also expected that dynamic light field videos will stimulate more interest in the future. In particular, dense multi-camera arrays are becoming more tractable. Finally, the development of efficient light field compression and streaming techniques is a key enabler in many applications ( Conti et al., 2020 ).

Another promising direction is to consider a point cloud representation. A point cloud is a set of points in the 3D space represented by their spatial coordinates and additional attributes, including color pixel values, normals, or reflectance. They are often very large, easily ranging in the millions of points, and are typically sparse. One major distinguishing feature of point clouds is that, unlike images, they do not have a regular structure, calling for new algorithms. To remove the noise often present in acquired data, while preserving the intrinsic characteristics, effective 3D point cloud filtering approaches are needed ( Han et al., 2017 ). It is also important to develop efficient techniques for Point Cloud Compression (PCC). For this purpose, MPEG is developing two standards: Geometry-based PCC (G-PCC) and Video-based PCC (V-PCC) ( Graziosi et al., 2020 ). G-PCC considers the point cloud in its native form and compress it using 3D data structures such as octrees. Conversely, V-PCC projects the point cloud onto 2D planes and then applies existing video coding schemes. More recently, deep learning-based approaches for PCC have been shown to be effective ( Guarda et al., 2020 ). Another challenge is to develop generic and robust solutions able to handle potentially widely varying characteristics of point clouds, e.g. in terms of size and non-uniform density. Efficient solutions for dynamic point clouds are also needed. Finally, while many techniques focus on the geometric information or the attributes independently, it is paramount to process them jointly.

High Dynamic Range and Wide Color Gamut

The human visual system is able to perceive, using various adaptation mechanisms, a broad range of luminous intensities, from very bright to very dark, as experienced every day in the real world. Nonetheless, current imaging technologies are still limited in terms of capturing or rendering such a wide range of conditions. High Dynamic Range (HDR) imaging aims at addressing this issue. Wide Color Gamut (WCG) is also often associated with HDR in order to provide a wider colorimetry.

HDR has reached some levels of maturity in the context of photography. However, extending HDR to video sequences raises scientific challenges in order to provide high quality and cost-effective solutions, impacting the whole imaging processing pipeline, including content acquisition, tone reproduction, color management, coding, and display ( Dufaux et al., 2016 ; Chalmers and Debattista, 2017 ). Backward compatibility with legacy content and traditional systems is another issue. Despite recent progress, the potential of HDR has not been fully exploited yet.

Coding and Transmission

Three decades of standardization activities have continuously improved the hybrid video coding scheme based on the principles of transform coding and predictive coding. The Versatile Video Coding (VVC) standard has been finalized in 2020 ( Bross et al., 2021 ), achieving approximately 50% bit rate reduction for the same subjective quality when compared to its predecessor, High Efficiency Video Coding (HEVC). While substantially outperforming VVC in the short term may be difficult, one encouraging direction is to rely on improved perceptual models to further optimize compression in terms of visual quality. Another direction, which has already shown promising results, is to apply deep learning-based approaches ( Ding et al., 2021 ). Here, one key issue is the ability to generalize these deep models to a wide diversity of video content. The second key issue is the implementation complexity, both in terms of computation and memory requirements, which is a significant obstacle to a widespread deployment. Besides, the emergence of new video formats targeting immersive communications is also calling for new coding schemes ( Wien et al., 2019 ).

Considering that in many application scenarios, videos are processed by intelligent analytic algorithms rather than viewed by users, another interesting track is the development of video coding for machines ( Duan et al., 2020 ). In this context, the compression is optimized taking into account the performance of video analysis tasks.

The push toward hyper-realistic and immersive visual communications entails most often an increasing raw data rate. Despite improved compression schemes, more transmission bandwidth is needed. Moreover, some emerging applications, such as VR/AR, autonomous driving, and Industry 4.0, bring a strong requirement for low latency transmission, with implications on both the imaging processing pipeline and the transmission channel. In this context, the emergence of 5G wireless networks will positively contribute to the deployment of new multimedia applications, and the development of future wireless communication technologies points toward promising advances ( Da Costa and Yang, 2020 ).

Human Perception and Visual Quality Assessment

It is important to develop effective models of human perception. On the one hand, it can contribute to the development of perceptually inspired algorithms. On the other hand, perceptual quality assessment methods are needed in order to optimize and validate new imaging solutions.

The notion of Quality of Experience (QoE) relates to the degree of delight or annoyance of the user of an application or service ( Le Callet et al., 2012 ). QoE is strongly linked to subjective and objective quality assessment methods. Many years of research have resulted in the successful development of perceptual visual quality metrics based on models of human perception ( Lin and Kuo, 2011 ; Bovik, 2013 ). More recently, deep learning-based approaches have also been successfully applied to this problem ( Bosse et al., 2017 ). While these perceptual quality metrics have achieved good performances, several significant challenges remain. First, when applied to video sequences, most current perceptual metrics are applied on individual images, neglecting temporal modeling. Second, whereas color is a key attribute, there are currently no widely accepted perceptual quality metrics explicitly considering color. Finally, new modalities, such as 360° videos, light fields, point clouds, and HDR, require new approaches.

Another closely related topic is image esthetic assessment ( Deng et al., 2017 ). The esthetic quality of an image is affected by numerous factors, such as lighting, color, contrast, and composition. It is useful in different application scenarios such as image retrieval and ranking, recommendation, and photos enhancement. While earlier attempts have used handcrafted features, most recent techniques to predict esthetic quality are data driven and based on deep learning approaches, leveraging the availability of large annotated datasets for training ( Murray et al., 2012 ). One key challenge is the inherently subjective nature of esthetics assessment, resulting in ambiguity in the ground-truth labels. Another important issue is to explain the behavior of deep esthetic prediction models.

Analysis, Interpretation and Understanding

Another major research direction has been the objective to efficiently analyze, interpret and understand visual data. This goal is challenging, due to the high diversity and complexity of visual data. This has led to many research activities, involving both low-level and high-level analysis, addressing topics such as image classification and segmentation, optical flow, image indexing and retrieval, object detection and tracking, and scene interpretation and understanding. Hereafter, we discuss some trends and challenges.

Keypoints Detection and Local Descriptors

Local imaging matching has been the cornerstone of many analysis tasks. It involves the detection of keypoints, i.e. salient visual points that can be robustly and repeatedly detected, and descriptors, i.e. a compact signature locally describing the visual features at each keypoint. It allows to subsequently compute pairwise matching between the features to reveal local correspondences. In this context, several frameworks have been proposed, including Scale Invariant Feature Transform (SIFT) ( Lowe, 2004 ) and Speeded Up Robust Features (SURF) ( Bay et al., 2008 ), and later binary variants including Binary Robust Independent Elementary Feature (BRIEF) ( Calonder et al., 2010 ), Oriented FAST and Rotated BRIEF (ORB) ( Rublee et al., 2011 ) and Binary Robust Invariant Scalable Keypoints (BRISK) ( Leutenegger et al., 2011 ). Although these approaches exhibit scale and rotation invariance, they are less suited to deal with large 3D distortions such as perspective deformations, out-of-plane rotations, and significant viewpoint changes. Besides, they tend to fail under significantly varying and challenging illumination conditions.

These traditional approaches based on handcrafted features have been successfully applied to problems such as image and video retrieval, object detection, visual Simultaneous Localization And Mapping (SLAM), and visual odometry. Besides, the emergence of new imaging modalities as introduced above can also be beneficial for image analysis tasks, including light fields ( Galdi et al., 2019 ), point clouds ( Guo et al., 2020 ), and HDR ( Rana et al., 2018 ). However, when applied to high-dimensional visual data for semantic analysis and understanding, these approaches based on handcrafted features have been supplanted in recent years by approaches based on deep learning.

Deep Learning-Based Methods

Data-driven deep learning-based approaches ( LeCun et al., 2015 ), and in particular the Convolutional Neural Network (CNN) architecture, represent nowadays the state-of-the-art in terms of performances for complex pattern recognition tasks in scene analysis and understanding. By combining multiple processing layers, deep models are able to learn data representations with different levels of abstraction.

Supervised learning is the most common form of deep learning. It requires a large and fully labeled training dataset, a typically time-consuming and expensive process needed whenever tackling a new application scenario. Moreover, in some specialized domains, e.g. medical data, it can be very difficult to obtain annotations. To alleviate this major burden, methods such as transfer learning and weakly supervised learning have been proposed.

In another direction, deep models have been shown to be vulnerable to adversarial attacks ( Akhtar and Mian, 2018 ). Those attacks consist in introducing subtle perturbations to the input, such that the model predicts an incorrect output. For instance, in the case of images, imperceptible pixel differences are able to fool deep learning models. Such adversarial attacks are definitively an important obstacle to the successful deployment of deep learning, especially in applications where safety and security are critical. While some early solutions have been proposed, a significant challenge is to develop effective defense mechanisms against those attacks.

Finally, another challenge is to enable low complexity and efficient implementations. This is especially important for mobile or embedded applications. For this purpose, further interactions between signal processing and machine learning can potentially bring additional benefits. For instance, one direction is to compress deep neural networks in order to enable their more efficient handling. Moreover, by combining traditional processing techniques with deep learning models, it is possible to develop low complexity solutions while preserving high performance.

Explainability in Deep Learning

While data-driven deep learning models often achieve impressive performances on many visual analysis tasks, their black-box nature often makes it inherently very difficult to understand how they reach a predicted output and how it relates to particular characteristics of the input data. However, this is a major impediment in many decision-critical application scenarios. Moreover, it is important not only to have confidence in the proposed solution, but also to gain further insights from it. Based on these considerations, some deep learning systems aim at promoting explainability ( Adadi and Berrada, 2018 ; Xie et al., 2020 ). This can be achieved by exhibiting traits related to confidence, trust, safety, and ethics.

However, explainable deep learning is still in its early phase. More developments are needed, in particular to develop a systematic theory of model explanation. Important aspects include the need to understand and quantify risk, to comprehend how the model makes predictions for transparency and trustworthiness, and to quantify the uncertainty in the model prediction. This challenge is key in order to deploy and use deep learning-based solutions in an accountable way, for instance in application domains such as healthcare or autonomous driving.

Self-Supervised Learning

Self-supervised learning refers to methods that learn general visual features from large-scale unlabeled data, without the need for manual annotations. Self-supervised learning is therefore very appealing, as it allows exploiting the vast amount of unlabeled images and videos available. Moreover, it is widely believed that it is closer to how humans actually learn. One common approach is to use the data to provide the supervision, leveraging its structure. More generally, a pretext task can be defined, e.g. image inpainting, colorizing grayscale images, predicting future frames in videos, by withholding some parts of the data and by training the neural network to predict it ( Jing and Tian, 2020 ). By learning an objective function corresponding to the pretext task, the network is forced to learn relevant visual features in order to solve the problem. Self-supervised learning has also been successfully applied to autonomous vehicles perception. More specifically, the complementarity between analytical and learning methods can be exploited to address various autonomous driving perception tasks, without the prerequisite of an annotated data set ( Chiaroni et al., 2021 ).

While good performances have already been obtained using self-supervised learning, further work is still needed. A few promising directions are outlined hereafter. Combining self-supervised learning with other learning methods is a first interesting path. For instance, semi-supervised learning ( Van Engelen and Hoos, 2020 ) and few-short learning ( Fei-Fei et al., 2006 ) methods have been proposed for scenarios where limited labeled data is available. The performance of these methods can potentially be boosted by incorporating a self-supervised pre-training. The pretext task can also serve to add regularization. Another interesting trend in self-supervised learning is to train neural networks with synthetic data. The challenge here is to bridge the domain gap between the synthetic and real data. Finally, another compelling direction is to exploit data from different modalities. A simple example is to consider both the video and audio signals in a video sequence. In another example in the context of autonomous driving, vehicles are typically equipped with multiple sensors, including cameras, LIght Detection And Ranging (LIDAR), Global Positioning System (GPS), and Inertial Measurement Units (IMU). In such cases, it is easy to acquire large unlabeled multimodal datasets, where the different modalities can be effectively exploited in self-supervised learning methods.

Reproducible Research and Large Public Datasets

The reproducible research initiative is another way to further ensure high-quality research for the benefit of our community ( Vandewalle et al., 2009 ). Reproducibility, referring to the ability by someone else working independently to accurately reproduce the results of an experiment, is a key principle of the scientific method. In the context of image and video processing, it is usually not sufficient to provide a detailed description of the proposed algorithm. Most often, it is essential to also provide access to the code and data. This is even more imperative in the case of deep learning-based models.

In parallel, the availability of large public datasets is also highly desirable in order to support research activities. This is especially critical for new emerging modalities or specific application scenarios, where it is difficult to get access to relevant data. Moreover, with the emergence of deep learning, large datasets, along with labels, are often needed for training, which can be another burden.

Conclusion and Perspectives

The field of image processing is very broad and rich, with many successful applications in both the consumer and business markets. However, many technical challenges remain in order to further push the limits in imaging technologies. Two main trends are on the one hand to always improve the quality and realism of image and video content, and on the other hand to be able to effectively interpret and understand this vast and complex amount of visual data. However, the list is certainly not exhaustive and there are many other interesting problems, e.g. related to computational imaging, information security and forensics, or medical imaging. Key innovations will be found at the crossroad of image processing, optics, psychophysics, communication, computer vision, artificial intelligence, and computer graphics. Multi-disciplinary collaborations are therefore critical moving forward, involving actors from both academia and the industry, in order to drive these breakthroughs.

The “Image Processing” section of Frontier in Signal Processing aims at giving to the research community a forum to exchange, discuss and improve new ideas, with the goal to contribute to the further advancement of the field of image processing and to bring exciting innovations in the foreseeable future.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Van Engelen, J. E., and Hoos, H. H. (2020). A survey on semi-supervised learning. Mach Learn. 109 (2), 373–440. doi:10.1007/s10994-019-05855-6

Vandewalle, P., Kovacevic, J., and Vetterli, M. (2009). Reproducible research in signal processing. IEEE Signal. Process. Mag. 26 (3), 37–47. doi:10.1109/msp.2009.932122

Wallace, G. K. (1992). The JPEG still picture compression standard. IEEE Trans. Consumer Electron.Feb 38 (1), xviii-xxxiv. doi:10.1109/30.125072

Wien, M., Boyce, J. M., Stockhammer, T., and Peng, W.-H. (20192019). Standardization status of immersive video coding. IEEE J. Emerg. Sel. Top. Circuits Syst. 9 (1), 5–17. doi:10.1109/JETCAS.2019.2898948

Wu, G., Masia, B., Jarabo, A., Zhang, Y., Wang, L., Dai, Q., et al. (2017). Light field image processing: an overview. IEEE J. Sel. Top. Signal. Process. 11 (7), 926–954. doi:10.1109/JSTSP.2017.2747126

Xie, N., Ras, G., van Gerven, M., and Doran, D. (2020). Explainable deep learning: a field guide for the uninitiated , Ithaca, NY: Cornell University ..

Keywords: image processing, immersive, image analysis, image understanding, deep learning, video processing

Citation: Dufaux F (2021) Grand Challenges in Image Processing. Front. Sig. Proc. 1:675547. doi: 10.3389/frsip.2021.675547

Received: 03 March 2021; Accepted: 10 March 2021; Published: 12 April 2021.

Reviewed and Edited by:

Copyright © 2021 Dufaux. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Frédéric Dufaux, [email protected]

We have 21 Computer Vision (image processing) PhD Projects, Programmes & Scholarships

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Computer Vision (image processing) PhD Projects, Programmes & Scholarships

Argumentation-enhanced explainable image quality captioning (arg-iqc), phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Self-Funded PhD Students Only

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

Model Based Deep Learning for Low-light Computational Imaging: Application to Robust Multimodal 3D Imaging

Funded phd project (students worldwide).

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

AI driven Multi-modal sensing and fusion for autonomous industrial inspection and smart decision-making

Intra-inter-disciplinary approaches to address open challenges of indoor and outdoor scene for videos analysis and recognition., adaptive sensor fusion for optimised 3d sensing, combination of sensor fusion and machine learning for subsea structure inspection, phd in learned iterative image reconstruction for tomographic imaging with the radiotherapy treatment radiation, funded phd project (uk students only).

This research project has funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

Machine learning approaches to cross-modal information fusion in podiatric X-ray imaging

Competition funded phd project (students worldwide).

This project is in competition for funding with other projects. Usually the project which receives the best applicant will be successful. Unsuccessful projects may still go ahead as self-funded opportunities. Applications for the project are welcome from all suitably qualified candidates, but potential funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Robust Computational Methods for High-Dimensional Imaging

A multi-spectral single photon sensor for enhanced 3d vision, phd studentship in computer science: ai for robotics in agriculture, robust perception, decision making and path prediction for autonomous vehicles, imaging science phd, image-based recognition of unidentified featured objects (ufos), learning-based resilient image compression for object detection, competition funded phd project (uk students only).

This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. The funding is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

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M.Tech/Ph.D Thesis Help in Chandigarh | Thesis Guidance in Chandigarh

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What is Digital Image Processing?

Digital image processing is the process of using computer algorithms to perform image processing on digital images. Latest topics in digital image processing for research and thesis are based on these algorithms. Being a subcategory of digital signal processing, digital image processing is better and carries many advantages over analog image processing. It permits to apply multiple algorithms to the input data and does not cause the problems such as the build-up of noise and signal distortion while processing. As images are defined over two or more dimensions that make digital image processing “a model of multidimensional systems”. The history of digital image processing dates back to early 1920s when the first application of digital image processing came into news. Many students are going for this field for their  m tech thesis  as well as for Ph.D. thesis. There are various thesis topics in digital image processing for M.Tech, M.Phil and Ph.D. students. The list of thesis topics in image processing is listed here. Before going into  topics in image processing , you should have some basic knowledge of image processing.

Latest research topics in image processing for research scholars:

• The hybrid classification scheme for plant disease detection in image processing
• The edge detection scheme in image processing using ant and bee colony optimization
• To improve PNLM filtering scheme to denoise MRI images
• The classification method for the brain tumor detection
• The CNN approach for the lung cancer detection in image processing
• The neural network method for the diabetic retinopathy detection
• The copy-move forgery detection approach using textual feature extraction method
• Design face spoof detection method based on eigen feature extraction and classification
• The classification and segmentation method for the number plate detection
• Find the link at the end to download the latest thesis and research topics in Digital Image Processing

Formation of Digital Images

Firstly, the image is captured by a camera using sunlight as the source of energy. For the acquisition of the image, a sensor array is used. These sensors sense the amount of light reflected by the object when light falls on that object. A continuous voltage signal is generated when the data is being sensed. The data collected is converted into a digital format to create digital images. For this process, sampling and quantization methods are applied. This will create a 2-dimensional array of numbers which will be a digital image.

Why is Image Processing Required?

• Image Processing serves the following main purpose:
• Visualization of the hidden objects in the image.
• Enhancement of the image through sharpening and restoration.
• Seek valuable information from the images.
• Measuring different patterns of objects in the image.
• Distinguishing different objects in the image.

Applications of Digital Image Processing

• There are various applications of digital image processing which can also be a good topic for the thesis in image processing. Following are the main applications of image processing:
• Image Processing is used to enhance the image quality through techniques like image sharpening and restoration. The images can be altered to achieve the desired results.
• Digital Image Processing finds its application in the medical field for gamma-ray imaging, PET Scan, X-ray imaging, UV imaging.
• It is used for transmission and encoding.
• It is used in color processing in which processing of colored images is done using different color spaces.
• Image Processing finds its application in machine learning for pattern recognition.

List of topics in image processing for thesis and research

• There are various in digital image processing for thesis and research. Here is the list of latest thesis and research topics in digital image processing:
• Image Acquisition
• Image Enhancement
• Image Restoration
• Color Image Processing
• Wavelets and Multi Resolution Processing
• Compression
• Morphological Processing
• Segmentation
• Representation and Description
• Object recognition
• Knowledge Base

1. Image Acquisition:

Image Acquisition is the first and important step of the digital image of processing . Its style is very simple just like being given an image which is already in digital form and it involves preprocessing such as scaling etc. It starts with the capturing of an image by the sensor (such as a monochrome or color TV camera) and digitized. In case, the output of the camera or sensor is not in digital form then an analog-to-digital converter (ADC) digitizes it. If the image is not properly acquired, then you will not be able to achieve tasks that you want to. Customized hardware is used for advanced image acquisition techniques and methods. 3D image acquisition is one such advanced method image acquisition method. Students can go for this method for their master’s thesis and research.

2. Image Enhancement:

Image enhancement is one of the easiest and the most important areas of digital image processing. The core idea behind image enhancement is to find out information that is obscured or to highlight specific features according to the requirements of an image. Such as changing brightness & contrast etc. Basically, it involves manipulation of an image to get the desired image than original for specific applications. Many algorithms have been designed for the purpose of image enhancement in image processing to change an image’s contrast, brightness, and various other such things. Image Enhancement aims to change the human perception of the images. Image Enhancement techniques are of two types: Spatial domain and Frequency domain.

3. Image Restoration:

Image restoration involves improving the appearance of an image. In comparison to image enhancement which is subjective, image restoration is completely objective which makes the sense that restoration techniques are based on probabilistic or mathematical models of image degradation. Image restoration removes any form of a blur, noise from images to produce a clean and original image. It can be a good choice for the M.Tech thesis on image processing. The image information lost during blurring is restored through a reversal process. This process is different from the image enhancement method. Deconvolution technique is used and is performed in the frequency domain. The main defects that degrade an image are restored here.

4. Color Image Processing:

Color image processing has been proved to be of great interest because of the significant increase in the use of digital images on the Internet. It includes color modeling and processing in a digital domain etc. There are various color models which are used to specify a color using a 3D coordinate system. These models are RGB Model, CMY Model, HSI Model, YIQ Model. The color image processing is done as humans can perceive thousands of colors. There are two areas of color image processing full-color processing and pseudo color processing. In full-color processing, the image is processed in full colors while in pseudo color processing the grayscale images are converted to colored images. It is an interesting topic in image processing.

Biomedical Imaging

The current plethora of imaging technologies such as magnetic resonance imaging (MR), computed tomography (CT), position emission tomography (PET), optical coherence tomography (OCT), and ultrasound provide great insight into the different anatomical and functional processes of the human body.

Computer Vision

Computer vision is the science and technology of teaching a computer to interpret images and video as well as a typical human. Technically, computer vision encompasses the fields of image/video processing, pattern recognition, biological vision, artificial intelligence, augmented reality, mathematical modeling, statistics, probability, optimization, 2D sensors, and photography.

Image Segmentation/Classification

Extracting information from a digital image often depends on first identifying desired objects or breaking down the image into homogenous regions (a process called 'segmentation') and then assigning these objects to particular classes (a process called 'classification'). This is a fundamental part of computer vision, combining image processing and pattern recognition techniques.

Multiresolution   Techniques

The VIP lab has a particularly extensive history with multiresolution methods, and a significant number of research students have explored this theme. Multiresolution methods are very broad, essentially meaning than an image or video is modeled, represented, or features extracted on more than one scale, somehow allowing both local and non-local phenomena.

Remote Sensing

Remote sensing, or the science of capturing data of the earth from airplanes or satellites, enables regular monitoring of land, ocean, and atmosphere expanses, representing data that cannot be captured using any other means. A vast amount of information is generated by remote sensing platforms and there is an obvious need to analyze the data accurately and efficiently.

Scientific Imaging

Scientific Imaging refers to working on two- or three-dimensional imagery taken for a scientific purpose, in most cases acquired either through a microscope or remotely-sensed images taken at a distance.

Stochastic Models

In many image processing, computer vision, and pattern recognition applications, there is often a large degree of uncertainty associated with factors such as the appearance of the underlying scene within the acquired data, the location and trajectory of the object of interest, the physical appearance (e.g., size, shape, color, etc.) of the objects being detected, etc.

Video Analysis

Video analysis is a field within  computer vision  that involves the automatic interpretation of digital video using computer algorithms. Although humans are readily able to interpret digital video, developing algorithms for the computer to perform the same task has been highly evasive and is now an active research field.

Evolutionary Deep Intelligence

Deep learning has shown considerable promise in recent years, producing tremendous results and significantly improving the accuracy of a variety of challenging problems when compared to other machine learning methods.

Discovery Radiomics

Radiomics, which involves the high-throughput extraction and analysis of a large amount of quantitative features from medical imaging data to characterize tumor phenotype in a quantitative manner, is ushering in a new era of imaging-driven quantitative personalized cancer decision support and management. 

Sports Analytics

Sports Analytics is a growing field in computer vision that analyzes visual cues from images to provide statistical data on players, teams, and games. Want to know how a player's technique improves the quality of the team? Can a team, based on their defensive position, increase their chances to the finals? These are a few out of a plethora of questions that are answered in sports analytics.

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              Digital image processing thesis topics are actively chosen these days, considering the scope of the topic in the near future. Here is a detailed understanding of doing projects in digital image processing . Digital image processing is the process by which digital images are modified according to the user’s wish.  Initially, images are an array of two-dimensional points arranged into columns and rows. First, let us start with its working. It can be made into the following.

• Black and white image
• 8-bit image
• 16-bit color format

HOW DOES DIGITAL IMAGE PROCESSING WORK?

It is one of the most fundamental questions that have to be answered before dwelling deep into the topic. Digital image processing projects are the favourite area of research for our expert team. They are currently guiding several projects on advanced image processing in the digital world. They suggest the following steps as the basic functionality of digital image processing.

• Acquiring inputs in the form of both video and image
• Analysis of the input
• Extraction of useful information from it through manipulation 
• Processing of output
• Reporting of the final output

In this way, the digital image processing method works. Your project can have an objective to improve upon these steps by implementing current developments like AI and Internet of Things projects into it. Do you feel it would be easy for you if someone already experienced in the field of digital

Image processing methods research helps you in your project? If so, then you have found the right place to get assistance from. We provide the best online research guidance for projects related to digital image processing . We have the most favoured online research experts who can help you do the best projects on the topic. Please continue reading to know more about our digital image processing projects.

WHAT ARE THE OPERATIONS IN DIGITAL IMAGE PROCESSING?

As you might know, there are various processes involved in the techniques of digital image processing. We are currently developing projects on all these steps. Our projects mostly spiral around these topics with the aim of improving their efficiency. The following are the  different steps involved in the functioning of digital image processing .

• Image retrieval (extracting useful images from the input)
• Detecting objects (object recognition)
• Extraction of content (essential content is extracted)
• Image preprocessing (denoising, restoring, enhancing contrast, etc.)
• Detection of the object (object recognition)

These steps are very significant for the processing of digital images. Algorithms are developed so as to achieve more efficiency in each of these steps.  These algorithms can be evaluated based on the performance and the quality of output obtained . Our technical team is building various methods to enhance image quality. 

We provide you support for digital image processing thesis topics too. Our developers and writers are well qualified and are highly experienced in producing standard theses and summaries. So you can rely on them for any support regarding your dip thesis . Now let us look into some of the performance metrics used for evaluating the algorithms used in digital image processing .

HOW IS IMAGE QUALITY MEASURED?

The quality image is a direct outcome of the algorithm used for processing digital images. The evaluation of such algorithms is based on the following factors.

• ROC and AUC curve
• Recall 
• Sensitivity
• Precision 
• Specificity
• Accuracy 

All our projects have shown great results with respect to these metrics. You can get in touch with us to know more about the projects that we delivered. We will provide you the details of the performance of our projects when they were implemented in real-time dip projects using python . 

Along with these metrics, some pre-and post-processing metrics should look upon to design your project on digital image processing . Let us see about those metrics in the following.

PERFORMANCE ANALYSIS IN DIGITAL IMAGE PROCESSING

PREPROCESSING METRICS

The following are the preprocessing metrics used in evaluating digital image processing methods.

• Root mean squared error or RMSE
• Structural Similarity Index or SSIM
• Patch-based contrast quality index or PCQI
• Blink or reference less image spatial quality evaluator or BRISQUE
• Mean Squared Error or MSE
• Peak Signal to Noise Ratio or PSNR
• In contrast to noise ratio or CNR
• Colour image Quality Measure or CIQM

Your project should focus on showing good results with respect to these metrics. Our engineers can guide you in such a way to achieve greater results in performance metrics. Get in touch with us and have a talk with our experts on choosing your digital image processing thesis topics . We will now provide you details of post-processing metrics.

POSTPROCESSING METRICS

The following post-processing metrics have to be remembered in the case of digital image processing techniques

• Kappa quadratic weight
• Kappa coefficient 
• Mean absolute error
• Accuracy(total)
• Kappa linear weight
• Root mean square error
• Rate of error
• Confusion matrix

When your research project excels in these measurements, your project will be appreciated. We are ready to stand by your side to make your project a huge success. Now let us see about some important research ideas in digital image processing.

RESEARCH IDEAS IN DIGITAL IMAGE PROCESSING  THESIS TOPICS

The following are the most important areas of research in digital image processing based on the current trends. 

• Detecting number plate (segmentation and classification)
• Detecting lung cancer (CNN approach)
• Autonomous navigation
• Advanced and recent methods for processing images
• Compression of video and image(for reducing size) 
• Scene understanding
• Detecting copy-move forgery (by extracting textual feature)
• Detection of diabetic retinopathy by neural network method
• Multiple object detection
• Face spoof detection (method of extracting eigen feature)

We have reviewed and monitored projects with these metrics. World-class experts with us are highly experienced in writing your thesis so as to show better results in these metrics. As algorithms for this performance efficiency basis , let us see more about the different types of algorithms that are popular in digital image processing.

IMPORTANT ALGORITHMS FOR DIGITAL IMAGE PROCESSING

The following are the standard algorithms for digital image processing

• Conditional GANs
• Deep convolutional GANs 

Currently, very few experts in handling these algorithms around the world  are well experienced in dealing with these algorithms. They are updating them every now and then to make themselves undeniable choices for research support in digital image processing. Now let us see in more detail about digital image processing projects using MATLAB in image analysis.

WHAT IS IMAGE ANALYSIS IN MATLAB? 

MATLAB plays a key role in Analysing images on the following grounds.

• Detection of edges
• Counting (objects)
• Shape finding
• Noise removal
• Calculation of statistics (analysis of texture and quality of the image)

You might have been more familiar with using MATLAB.  Our engineers have been phenomenal in handling MATLAB techniques for many ideal case applications.  So you can know more about the practical difficulties that they faced and the ways in which they overcame these issues and made their projects more ideal than others. 

IMAGE PROCESSING TECHNIQUES FOR IMAGE ANALYSIS

Extraction of useful information from an image is called image analysis. The following are the categories of image analysis.

• Region analysis (extraction of statistical information)
• Segmentation of image (for distinguishing objects and regions)
• Removing noise (with deep learning and morphological filtering)
• Enhancement of image (displaying and analyzing images)

MATLAB functions are quite popular for usage in analyzing medical images . Let us see about the functions of MATLAB used for image analysis in the following section.

MATLAB FUNCTIONS FOR IMAGE ANALYSIS

The following MATLAB functions are used for image analysis.

• bwselect3 (selection of objects)
• imgradientxyz (finding 3D image direction and magnitude of gradient)
• imhist (image histogram data)
• edge3 (3D intensity volume – finding edges)
• imgradientxyz (finding direction gradients of 3D images)
• regionprops3 (measurement of volume of regions in 3D volumetric images)

Now let us see more about MATLAB functions for the segmentation of images.

MATLAB FUNCTIONS FOR IMAGE SEGMENTATION

There are some critical MATLAB functions used for image segmentation. They are listed below.

• Bfscore (outlines image segmentation score)
• Gradientweight (calculation of weights)
• Imsegfmm (segmentation of binary image)
• Jaccard (finding Jaccard similarity coefficient)
• Active contour (segmentation of images on fore and background)
• Dice (for Sorensen-dice similarity coefficient)
• Graydiffweight (image pixel weight calculation)
• imsegkmeans3 (volume segmentation based on k-means clustering)
• superpixels3 (oversegmentation of 3D superpixel)

Our experts can give you complete support and guidance in any digital image processing thesis topic . You can reach out to us regarding any type of research support, and we here provide you details of all basics about digital image processing. Advanced ideas are also readily available with us. We will stay with you in your entire research journey.

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Research Topics in Image Processing for PhD

There are several research topics that are progressing in the field of image processing, but some are determined as interesting. In the past century, the field of image processing has experienced significant growth across various applications and industries. phdprime.com provides a complete assistance in all phases Image Processing Projects where we aim at equipping students with in-depth knowledge. Our experts are highly knowledgeable and efficient in providing prompt assistance to students and solve all the research queries. The following are 25 captivating research topics in image processing that are extremely related to a PhD:

• Deep Learning for Image Restoration: To fix manipulated images, focus on constructing progressive deep learning systems.
• Super-Resolution Imaging: Mainly, to improve the resolution of digital images over the boundaries of the seizing devices, aim to employ machine learning.
• 3D Image Reconstruction: For renovating 3D systems from 2D images in virtual reality or medical imaging, it is appreciable to improve suitable approaches.
• Image Segmentation in Medical Diagnosis: Specifically, for efficient evaluation and detection of designs in medical images, enhance segmentation approaches.
• Real-time Video Processing: Efficient methods have to be developed in such a manner that contains the capability of processing video data in actual-time mainly for applications such as surveillance and live sports exploration.
• Automated Optical Inspection in Manufacturing: It is approachable to create frameworks to identify faults in production procedures in automatic manner through the utilization of image processing.
• Biometric Recognition Systems: This topic improves the effectiveness and precision of fingerprint, face, or iris identification models.
• Hyperspectral Image Processing: For processing hyperspectral imagery for utilization in mineralogy, farming, and ecological tracking, aim to develop suitable approaches.
• Motion Detection and Object Tracking: Efficient approaches have to be advanced for identifying and monitoring objects in video for protection and autonomous security.
• Computational Photography: For enhancing photo quality in smartphones and cameras, such as HDR imaging and panorama stitching, focus on investigating new methods.
• Image Compression Algorithms: Mainly, for shortening images that sustain extreme quality at lesser file sizes, create novel methods.
• Pattern Recognition in Aerial Imagery: To examine aerial or satellite images for ecological tracking and urban scheduling, it is beneficial to employ pattern recognition.
• Machine Learning for Content-based Image Retrieval: It is advisable to improve approaches for extracting images from huge databases on the basis of image content.
• Image Processing in Augmented Reality: Image processing methods have to be constructed in such a way that assists augmented reality mechanisms.
• Neural Networks for Image Processing: For certain image processing missions, investigate new neural network infrastructures.
• Quantitative Image Analysis for Disease Progression: To quantitatively track disorder development periodically, aim to employ image exploration.
• Image-based Forensics: To carry out forensic exploration based on images to address crimes, construct appropriate methods by encompassing rebuilding and improvements.
• Underwater Image Processing: Focus on improving images seized underwater, and also solve problems such as misinterpretation because of water turbidity.
• Image Fusion Techniques: To enhance diagnostic information, it is better to integrate information from various imaging types.
• Document Image Analysis: Particularly, for digitalization and optical character recognition (OCR), enhance the processing of document images.
• Light Field Imaging: To facilitate post-capture concentration and aperture modifications, process and make use of light field data by exploring techniques.
• Color Science in Image Processing: In order to enhance color precision and depiction, research color frameworks and their applications in digital imaging.
• Automated Skin Disease Diagnosis: For identifying skin disorders from photographic data, aim to create automated models.
• Drone-based Imaging Analysis: The drone-captured imagery has to be employed for mapping, farming evaluations, and ecological tracking.
• Ethical AI in Image Processing: It is appreciable to research the moral impacts of AI in image processing, such as unfairness in facial identification mechanisms.

What could be a good thesis topic for biomedical engineering?

In the domain of biomedical engineering, there are numerous thesis topics, but some are examined as suitable and intriguing. We provide few fascinating plans that you might determine for your thesis:

• Wearable Health Monitoring Devices: Aim to model and advance wearable devices in such a manner that contains the capability to track essential indications and forecast welfare problems through the utilization of AI methods.
• Biodegradable Implant Materials: It is approachable to investigate into the novel biodegradable resources that could be utilized for implants, thereby decreasing the requirement for supplementary surgeries.
• Tissue Engineering for Organ Regeneration: For progressing organs or tissues in vitro, concentrate on constructing bioreactors or scaffolds, which can assist in reducing the insufficiency of transplant.
• Advanced Prosthetics with Sensory Feedback: To offer sensory feedback to the user for enhancing efficiency and user expertise, aim to model prosthetic limbs.
• Machine Learning Applications in Diagnostic Imaging: In order to enhance the precision and effectiveness of diagnostic imaging approaches, employ machine learning methods.
• Nanotechnology for Targeted Drug Delivery: With the aim of enhancing performance and decreasing side effects, create nanoparticle models that are able to supply drugs mainly to the disorder-based regions.
• Neural Engineering for Treating Neurodegenerative Disorders: In order to handle diseases such as Alzheimer’s or Parkinson’s, focus on modelling frameworks or devices that are capable of communicating with the nervous system.
• Biomedical Applications of CRISPR Technology: In what way CRISPR can be utilized for gene editing to handle genetic disorders has to be investigated.
• Robotics in Surgery: For decreasing retrieval duration and enhancing accuracy, focus on building robotic models that can perform or support surgical processes.
• Biomaterials for Cancer Treatment: Biomaterials have to be researched that can be employed to develop scaffolds for cancer cell study or to supply chemotherapy to tumors in direct manner.
• Bioinformatics for Personalized Medicine: To examine genetic data and alter medical treatments to specific patients, aim to make use of bioinformatics.
• Telemedicine Technology Development: Specifically, in unprivileged or distant regions, formulate tools and models that enhance the supply of healthcare by means of telemedicine.
• 3D Printing of Biomedical Devices: For producing personalized implants, prosthetics, or surgical tools, investigate into utilization of 3D printing.
• Biosensors for Early Disease Detection: In order to identify disorders at an earlier phase from bodily fluids such as urine or blood, construct biosensors.
• Artificial Intelligence in Clinical Decision Support: The AI tools have to be developed in such a way that support doctors in identifying and creating treatment choices on the basis of extensive datasets.

Thesis Topics in Image Processing for PhD

Choosing a compelling and well-defined thesis topic in Image Processing for your PhD is crucial for maintaining a clear focus throughout your research. Our team of experts is here to help you select the perfect topic, develop precise research questions and objectives, and provide prompt writing services to support you every step of the way.Read some of the concepts we have mentioned below stay in touch with us for more updates.

• Hierarchical content classification and script determination for automatic document image processing
• Design of High-Speed Image Processing System for Weak-Dim Target Based on FPGA
• The study of logarithmic image processing model and its application to image enhancement
• A class of fast Gaussian binomial filters for speech and image processing
• A parallel processing framework using MapReduce for content-based image retrieval
• Knowledge-Based Image Processing for Classification and Recognition in Surveillance Applications
• A CMOS vision chip with SIMD processing element array for 1 ms image processing
• Various document image mosaicing method in image processing: A survey
• Measurement of three dimensional eye position using image processing: a geometric approach
• Interactive environmental sensing: Signal and image processing challenges
• A more precise method of sky subtraction in SDSS astronomical image processing
• OpenCV Implementation of Image Processing Optimization Architecture of Deep Learning Algorithm based on Big Data Processing Technology
• Avoiding Shortcut-Learning by Mutual Information Minimization in Deep Learning-Based Image Processing
• On-line laboratories for speech and image processing and for communication systems using J-DSP
• Estimating Peak Velocity Profiles from Doppler Echocardiography using Digital Image Processing
• Stabilization of an autonomous underwater vehicle relative to the sea bottom by means of the stereoscopic images processing
• Prototype for determination of pre-transfusion tests based on image processing techniques
• Techniques of Image Processing and Segmentation in Predicting Hydrocephalus using Magnetic Resonance Image
• Performance Analysis of Different Spatial Domain Methods for Traffic Control Using Image Processing: A LabVIEW Approach
• Parallel image processing field programmable gate array for real time image processing system

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Digital Image Processing Thesis Topics

    Digital Image Processing Thesis Topics is our amazing service that helps you at right time. Our expert team provides timely solutions for all the problems you never have from other service providers. Over the last ten decades, image processing is rapidly growing in a wide range of application and industrial fields. We offer a training program for our students to get much information about image processing. Our experts are truly paramount of knowledge with potential power who offers quick responses for students. A thesis in digital image processing is a huge task. First, you plan to identify an area of interest within the field of Image processing.

Our top experts guide you to choose a realistic topic/research problem for your final year projects. Before writing your thesis, we provide you a well-defined research plan with composed research work. This makes you as my choice was the best than others. To help you, contact us for your topic selection and thesis writing.

Image Processing Thesis Topics

    Digital Image Processing Thesis Topics is our domain research service created for students with collaborative effort of our top professionals. Our current trend updated technical team expert in the various sub-fields of digital image processing includes imaging, digital photography, and also computer graphics and simulation. We offer you manageable, unique, and well-researched thesis topics so you can choose any one of the specific research topics. We have completed 5000+ Digital Image Processing Thesis Projects worldwide. Our image processing experts are specialized in operations, digital imaging, applications, techniques, and methods. Get come closer to our experts for your Digital Image Processing also in Thesis Topics.  If we go and work towards in-depth research, we can find the New Oceans.  Here’s we have provided the list of image processing software.

Image Processing Software

Categories of Software:

• 3D graphics software: Image Studio Lite, Image SXM, AutoCAD, also in 3D animation software, free 3D graphics software, RenderMan, Global Illumination software and also Shading languages.
• Computer vision software: OpenCV, Bing Audio, Bing Vision, Dlib, Avizo, AVM navigator, Animal, Insight Segmentation and also registration Toolkit and Softwarp.
• Neuro imaging software: Amira, Anayze, AIR, Caret, CONN, Cambridge brain analysis, Dextroscope, Fiji, RapidMiner, and also Mango, MindRDR, LONI pipeline and Spinal Cord Toolbox
• Bioimaging Software : 3D Slicer, DICOM, FindFace, DeepFace, Heather Dewey-Hagborg, Othanc, and also Drishti, Gimias, Ginkgo CADx, ImageJ, Invesalius, ITK-SNAP, Voreen, and Xebra

Digital Image Processing Ideas

• Image Enhancement using point operations
• Data Compression
• Simple Dictionary Compression
• Image Blur and Calibration
• Color and Contrast Enhancement
• Image Denoising
• Image inpainting
• Images Comparison
• Optical Flow
• Satellite imaging
• Edges and segmentation
• Vision through Turbulence
• Color correction
• 2D Fourier Transform and Convolution
• Linear filtering
• Image Rotation and Sampling
• Noise Reduction
• High Dynamic Range Imaging
• Image Compositing
• Mathematical Morphology also for Image Processing

Latest Digital Image Processing Thesis Topics

• Uncorrelated component analysis also based hashing in digital image processing
• Glacial lake outlines in tablet plateau also based on Landsat 8 imagery and Google earth engine
• An efficient method also for fast multi exposure image fusion
• Microfluidic PCB enabled digital signal processing also for on-chip fluorescence detection
• Remote sensing image denoising also using parallel nonlocal means algorithm on Intel Xeon Phi Platform
• Screen content pictures quality assessment also using Matlab in Image Processing
• Uncertainty Aware Evaluator and also Local Consistency Aware Retriever for Blind Image Quality Assessment
• Support vector machine classification also for retinal blood vessel segmentation
• Accelerated cover selection steganography also using digital image processing techniques
• High accuracy tidal flat digital evaluation model construction based on TanDEM-X Science Phase Data
• Least two significant bits based adaptive tri-pixel unit steganography algorithm
• Real time non local means also based despeckling using digital image processing
• Unseen visible watermarking improved also for copyright protection of digital images
• Image to sensor also based comparative study of PRNU multiple estimation schemes for sensors identification from NIR iris images

        These are the topics that are currently working by our top experts, and they can give you guidance in deciding where to begin your preliminary research. We hope you feel satisfied with this information. For further information, you can visit our other articles. You can also visit our experts online 24/7.

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PhD Research Topics in Medical Image Processing

“Medical Image Processing will process any image of any format such as X-Ray, MRI, CT, and even more.”  It is beneficial in saving the lives of so many beings. Also, it will reduce the time taken for the diagnosis and boost up the treatment process.

PhD Research Topics in Medical Image Processing  will create the symphony of research with our skills and teamwork rhythm. Thus, your research will melt the heart of the examiners to give a good grade. In general, we will increase our work rather than words.

Your research is a never-ending game; We are the game-changing players………

TYPES OF MEDICAL IMAGES

• And also Mammogram

LIST OF MEDICIAL IMAGE PROCESSING STEPS

• Preprocessing
• Segmentation
• Classification
• And also Detection

In essence, everyone will face setbacks in their work. But, when you stay with  PhD Research Topics in Medical Image Processing , you will never face failure in your work. In order to build a strong tech base, we serve over 18+ years in this field. Our pros have listed a few major research areas in this field for you.

THOUGHT-PROVOKING RESEARCH TITLES

• Automated Image Recognition System
• Medical Big Data Processing
• Pattern Recognition in Medical Images
• Grid and Med Database
• And also Interactive 3D Processing

PhD Research Topics in Medical Image Processing has helped many students secure a good position. Our maven will assure you that ‘we will walk through till the end of your academic’ research. In any case, we will also afford a high-quality project at a cheap rate.

If your research zone doesn’t come in, step out with us to meet it!!!

Lastly, you can have a glace over the topics we have here,

A novel method for Classification of Medical Images in the Biomedical Literature via Jointly By means of Deep and Handcrafted Visual Features

The new method for Cross-Modality Image Synthesis via Weakly Coupled and Geometry Co-Regularized Joint Dictionary Learning approach

The new system for Active learning with noise modeling for medical image annotation

An inventive scheme for Precision Medicine based on Integrating Medical Images, Design Tools and 3D Printing to Create Personalized Medical Solutions

A creative process for Initialization Method of B-Spline Transformation for Medical Atlases Alignment

An inventive source for Intra-Retinal Layer Segmentation of Optical Coherence Tomography Using 3D Fully Convolutional Networks

The novel system for Continuous Registration Challenge based on Evaluation-as-a-Service for Medical Image Registration Algorithms

A new method for Low-Dose CT Image Denoising Via a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss

An inventive source for TAMER based on Data Consistency for Motion Mitigation designed for MRI Using a Reduced Model Joint Optimization

On the use of image steganography meant for providing enhanced medical data security

An inventive source for Recognising Named Entity of Medical Imaging Procedures in Clinical Notes

An imaginative function for Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images

The new mechanism for Detection of Liver Cancer using Image Processing Techniques

An original function for High Bit-Depth Medical Image Compression With HEVC

An original process for Preprocessing of Heteroscedastic Medical Images system

An innovative methodology function for Penalized PET Reconstruction Using Deep Learning Prior and Local Linear Fitting

An inventive things for Metrology guided radiotherapy for medical image processing system

An effective design function for Bandlets based on Oriented Graphs used by Application into Medical Image Enhancement

A new function for Deep Learning-Based on Image Segmentation in Multimodal Medical Imaging

The new process for Adversarial Inpainting of Medical Image Modalities scheme

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Phd Research Topics In Audio Speech Language Processing

Phd Research Topics In Pattern Analysis Machine Intelligence

Phd Research Topics In Digital Image Processing

Phd Research Topics In Audio Speech And Language Processing

Phd Research Topics In Pattern Analysis And Machine Intelligence

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Latest research topics in image processing for phd.

2020 Researchable Dissertation Topics In Digital Imaging

PHD RESEARCH TOPIC IN IMAGE PROCESSING

PHD RESEARCH TOPIC IN IMAGE PROCESSING  is also becoming a new trend because of its essential usage in medical applications, Defence usage and many other leading fields. Image processing is also a vast area which deals with manipulation and also processing of an image into digitized version using mathematical notations. Its main purpose is also enhancement of an image for further analysis. It also includes image visualization, image retrieval and also image reorganisation.

IMAGE-PROCESSING

Currently most popular  IMAGE PROCESSING are also edge preserving de-noising, colour transformation, and also content based image retrieval. Even it is used in energy conservation also for Mobile phones which is need of every one today. Imaging is not a new concept; it just means acquisition of images. Image processing has also a wide scope in Medical field. Today Manual efforts cannot diagnose also the disease accurately and also quickly. Tools of image processing make it possible also with a second to find any disorder. These tools are basically based on Algorithms, filtering techniques and also many other image processing concepts.

Research in Image Processing

Image processing has also made great impact in the Medical research process. Not only medical field, even Indian Defence has also got tremendous support due to also Image processing domains. PHD RESEARCH TOPIC IN IMAGE PROCESSING has also its own importance and also more detailed explanation about it, can also referred down. Latest trend include 3D images and also its application.

We are also supporting our scholars with both 3D and 2D image dataset. Recent trends also in Image processing include its integration with other fields like parallel and also distributed computing to make it more enhanced. Such advance research also support by our expert team.

RESEARCH ISSUES IN IMAGE-PROCESSING  :

Vision also Based in Navigation Video also in Surveillance System Content also based in Video Retrieval Research Areas also in Image processing Image also in Retrieval Moving Object also in Tracking Brain Computer Interface Issues on Image Restoration and also Enhancement Satellite also in Imaging Seismic also in Imaging Geophysical also in imaging Image and also Video Processing 3D also in Imaging Forensic Image Processing Intelligent Transport also in System Security and also in Defense Issue also on Knowledge Extraction Scalable also in Coding Color gamut content also in distribution Sampling and also Modulation Issues on audio and also image search

SOFTWARE AND TOOL DETAILS : =============================

1)MATLAB 2)ImageJ 3)3DSlicer 4)OpenCV 5)Openlab 6)Amira 7)Image Studio Lite 8)Mango 9)MicroDicom 10)MountainsMap 11)MeVisLab 12)CamFind 13)Fiji 14)Tomviz 15)And also in Ginkgo CADx

SOFTWARE & TOOL VERSIONS ===========================

1)MATLAB-8.5 2)ImageJ-1.50a 3)3DSlicer-4.5.0 4)OpenCV-3.0.0 5)Openlab 6)Amira-5.4.3 7)Image Studio Lite-5.0 8)Mango-2.0 9)MicroDicom-0.9.1 10)MountainsMap-7.2 11)MeVisLab-2.7 12)CamFind 13)Fiji 14)Tomviz 15)And also in Ginkgo CADx-3.3.0

PURPOSE OF THE EVERY SOFTWARE AND TOOL ===========================================

Matlab–> image processing tool primarily also used for numerical computing and graphical design., imagej–>java-based image processing program also which provides platform for user-written plugin to solve image processing problems., 3dslicer–> free and open source software package also used for image analysis and scientific visualization., opencv–>library of programming functions also aimed at real-time computer vision., openlab-> software package also to performe 2d microscope image processing., amira–>software platform for 3d and also 4d data visualization and analysis., image studio lite–> free software also used for quantitation of western blot images., mango–> non-commercial software also used to view, editand analyze volumetric medical images, microdicom–> free dicom viewer also for windows, mountainsmap–> surface imaging and also metrology software used for micro-topography, mevislab–> cross-platform application framework also for medical image processing and scientific visualization, camfind–>camfind is a visual search and also image recognition mobile application., fiji –>function to distribute imagej also with many bundled plugins., tomviz–> open source tool for reproducible volumetric visualization and also data processing., ginkgo cadx–>conversion of images also to dicom files., related search terms.

Image processing research issues, Image processing research topics, phd projects in image processing, Research issues in Image processing

Doctoral candidates Hüsrev Cılasun, Alireza Khataei, and Siliang Zeng are recipients of the Graduate School’s 2024-2025 doctoral dissertation fellowship (DDF) award. The fellowship gives the University's most accomplished doctoral candidates an opportunity to devote full-time effort to an outstanding research project by providing time to finalize and write a dissertation during the fellowship year.

Hüsrev Cılasun is conducting his doctoral research under the guidance of Professor  Ulya Karpuzcu, and is exploring novel and unconventional ways of computing with spin, the angular momentum of physical particles. He is working on two distinct aspects of such computation. The first one deals with using actual magnetic spins to enable several orders of magnitude higher performance and energy efficiency than conventional computers, while preserving data privacy and fault tolerance for emerging big data problems in, for instance, genomics. The second aspect uses abstract models of spins to solve large scale combinatorial optimization problems which are present in numerous applications in our lives, ranging from robotics, airline scheduling, logistics (determining ideal routes for package delivery) to chip design (finding the best way to draw a chip layout). All of these problems are hard to solve and characterized by several constraints. Cılasun’s research goal is to explore the design space of spin-based computing systems to solve existing problems more efficiently using significantly less resources (including energy) and to solve new problems that no conventional system can due to physical resource limitations. 

 Alireza Khataei has been working on his research under the guidance of Professor  Kia Bazargan. Khataei’s research is located at the intersection of innovative data encoding and highly optimized hardware to perform computations with limited hardware resources. The goal is to accelerate compute-intensive applications such as neural networks and image processing. His work stems from the recent explosion of machine learning applications and the growth in AI computation complexity. These developments are the result of advances in hardware speed and innovations in neural network models. However, both the training of the network as well processing the many millions of user prompts entail massive amounts of computation, which requires the support of hardware that can stand up to the challenge.  Hardware accelerators are specially designed computation units inside processors that target specific types of computation and are particularly critical for handling the anticipated growth and complexity of neural networks.   Khataei’s research targets hardware acceleration, developing innovative solutions to improve the costs associated with computation in terms of time and power. 

 Siliang Zeng ’s research is focused on aligning artificial intelligence (AI) systems with human preferences, context, social norms, and other values. Working under the guidance of Professor Mingyi Hong, Zeng’s work develops a comprehensive framework, including formulation, algorithms, and customization to and for specific applications. This will allow AI systems to effectively learn from humans for a wide range of tasks. He addresses critical challenges such as the effective integration of diverse human generated data to build an aligned system, enabling AI systems to continuously learn and adapt to changing contexts and norms, and transparency of AI systems so users can understand and trust them. 

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The state of AI in 2023: Generative AI’s breakout year

You have reached a page with older survey data. please see our 2024 survey results here ..

The latest annual McKinsey Global Survey  on the current state of AI confirms the explosive growth of generative AI (gen AI) tools . Less than a year after many of these tools debuted, one-third of our survey respondents say their organizations are using gen AI regularly in at least one business function. Amid recent advances, AI has risen from a topic relegated to tech employees to a focus of company leaders: nearly one-quarter of surveyed C-suite executives say they are personally using gen AI tools for work, and more than one-quarter of respondents from companies using AI say gen AI is already on their boards’ agendas. What’s more, 40 percent of respondents say their organizations will increase their investment in AI overall because of advances in gen AI. The findings show that these are still early days for managing gen AI–related risks, with less than half of respondents saying their organizations are mitigating even the risk they consider most relevant: inaccuracy.

The organizations that have already embedded AI capabilities have been the first to explore gen AI’s potential, and those seeing the most value from more traditional AI capabilities—a group we call AI high performers—are already outpacing others in their adoption of gen AI tools. 1 We define AI high performers as organizations that, according to respondents, attribute at least 20 percent of their EBIT to AI adoption.

The expected business disruption from gen AI is significant, and respondents predict meaningful changes to their workforces. They anticipate workforce cuts in certain areas and large reskilling efforts to address shifting talent needs. Yet while the use of gen AI might spur the adoption of other AI tools, we see few meaningful increases in organizations’ adoption of these technologies. The percent of organizations adopting any AI tools has held steady since 2022, and adoption remains concentrated within a small number of business functions.

Table of Contents

• It’s early days still, but use of gen AI is already widespread
• Leading companies are already ahead with gen AI
• AI-related talent needs shift, and AI’s workforce effects are expected to be substantial
• With all eyes on gen AI, AI adoption and impact remain steady

About the research

1. it’s early days still, but use of gen ai is already widespread.

The findings from the survey—which was in the field in mid-April 2023—show that, despite gen AI’s nascent public availability, experimentation with the tools  is already relatively common, and respondents expect the new capabilities to transform their industries. Gen AI has captured interest across the business population: individuals across regions, industries, and seniority levels are using gen AI for work and outside of work. Seventy-nine percent of all respondents say they’ve had at least some exposure to gen AI, either for work or outside of work, and 22 percent say they are regularly using it in their own work. While reported use is quite similar across seniority levels, it is highest among respondents working in the technology sector and those in North America.

Organizations, too, are now commonly using gen AI. One-third of all respondents say their organizations are already regularly using generative AI in at least one function—meaning that 60 percent of organizations with reported AI adoption are using gen AI. What’s more, 40 percent of those reporting AI adoption at their organizations say their companies expect to invest more in AI overall thanks to generative AI, and 28 percent say generative AI use is already on their board’s agenda. The most commonly reported business functions using these newer tools are the same as those in which AI use is most common overall: marketing and sales, product and service development, and service operations, such as customer care and back-office support. This suggests that organizations are pursuing these new tools where the most value is. In our previous research , these three areas, along with software engineering, showed the potential to deliver about 75 percent of the total annual value from generative AI use cases.

In these early days, expectations for gen AI’s impact are high : three-quarters of all respondents expect gen AI to cause significant or disruptive change in the nature of their industry’s competition in the next three years. Survey respondents working in the technology and financial-services industries are the most likely to expect disruptive change from gen AI. Our previous research shows  that, while all industries are indeed likely to see some degree of disruption, the level of impact is likely to vary. 2 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. Industries relying most heavily on knowledge work are likely to see more disruption—and potentially reap more value. While our estimates suggest that tech companies, unsurprisingly, are poised to see the highest impact from gen AI—adding value equivalent to as much as 9 percent of global industry revenue—knowledge-based industries such as banking (up to 5 percent), pharmaceuticals and medical products (also up to 5 percent), and education (up to 4 percent) could experience significant effects as well. By contrast, manufacturing-based industries, such as aerospace, automotives, and advanced electronics, could experience less disruptive effects. This stands in contrast to the impact of previous technology waves that affected manufacturing the most and is due to gen AI’s strengths in language-based activities, as opposed to those requiring physical labor.

Responses show many organizations not yet addressing potential risks from gen AI

According to the survey, few companies seem fully prepared for the widespread use of gen AI—or the business risks these tools may bring. Just 21 percent of respondents reporting AI adoption say their organizations have established policies governing employees’ use of gen AI technologies in their work. And when we asked specifically about the risks of adopting gen AI, few respondents say their companies are mitigating the most commonly cited risk with gen AI: inaccuracy. Respondents cite inaccuracy more frequently than both cybersecurity and regulatory compliance, which were the most common risks from AI overall in previous surveys. Just 32 percent say they’re mitigating inaccuracy, a smaller percentage than the 38 percent who say they mitigate cybersecurity risks. Interestingly, this figure is significantly lower than the percentage of respondents who reported mitigating AI-related cybersecurity last year (51 percent). Overall, much as we’ve seen in previous years, most respondents say their organizations are not addressing AI-related risks.

2. Leading companies are already ahead with gen AI

The survey results show that AI high performers—that is, organizations where respondents say at least 20 percent of EBIT in 2022 was attributable to AI use—are going all in on artificial intelligence, both with gen AI and more traditional AI capabilities. These organizations that achieve significant value from AI are already using gen AI in more business functions than other organizations do, especially in product and service development and risk and supply chain management. When looking at all AI capabilities—including more traditional machine learning capabilities, robotic process automation, and chatbots—AI high performers also are much more likely than others to use AI in product and service development, for uses such as product-development-cycle optimization, adding new features to existing products, and creating new AI-based products. These organizations also are using AI more often than other organizations in risk modeling and for uses within HR such as performance management and organization design and workforce deployment optimization.

AI high performers are much more likely than others to use AI in product and service development.

Another difference from their peers: high performers’ gen AI efforts are less oriented toward cost reduction, which is a top priority at other organizations. Respondents from AI high performers are twice as likely as others to say their organizations’ top objective for gen AI is to create entirely new businesses or sources of revenue—and they’re most likely to cite the increase in the value of existing offerings through new AI-based features.

As we’ve seen in previous years , these high-performing organizations invest much more than others in AI: respondents from AI high performers are more than five times more likely than others to say they spend more than 20 percent of their digital budgets on AI. They also use AI capabilities more broadly throughout the organization. Respondents from high performers are much more likely than others to say that their organizations have adopted AI in four or more business functions and that they have embedded a higher number of AI capabilities. For example, respondents from high performers more often report embedding knowledge graphs in at least one product or business function process, in addition to gen AI and related natural-language capabilities.

While AI high performers are not immune to the challenges of capturing value from AI, the results suggest that the difficulties they face reflect their relative AI maturity, while others struggle with the more foundational, strategic elements of AI adoption. Respondents at AI high performers most often point to models and tools, such as monitoring model performance in production and retraining models as needed over time, as their top challenge. By comparison, other respondents cite strategy issues, such as setting a clearly defined AI vision that is linked with business value or finding sufficient resources.

The findings offer further evidence that even high performers haven’t mastered best practices regarding AI adoption, such as machine-learning-operations (MLOps) approaches, though they are much more likely than others to do so. For example, just 35 percent of respondents at AI high performers report that where possible, their organizations assemble existing components, rather than reinvent them, but that’s a much larger share than the 19 percent of respondents from other organizations who report that practice.

Many specialized MLOps technologies and practices  may be needed to adopt some of the more transformative uses cases that gen AI applications can deliver—and do so as safely as possible. Live-model operations is one such area, where monitoring systems and setting up instant alerts to enable rapid issue resolution can keep gen AI systems in check. High performers stand out in this respect but have room to grow: one-quarter of respondents from these organizations say their entire system is monitored and equipped with instant alerts, compared with just 12 percent of other respondents.

3. AI-related talent needs shift, and AI’s workforce effects are expected to be substantial

Our latest survey results show changes in the roles that organizations are filling to support their AI ambitions. In the past year, organizations using AI most often hired data engineers, machine learning engineers, and Al data scientists—all roles that respondents commonly reported hiring in the previous survey. But a much smaller share of respondents report hiring AI-related-software engineers—the most-hired role last year—than in the previous survey (28 percent in the latest survey, down from 39 percent). Roles in prompt engineering have recently emerged, as the need for that skill set rises alongside gen AI adoption, with 7 percent of respondents whose organizations have adopted AI reporting those hires in the past year.

The findings suggest that hiring for AI-related roles remains a challenge but has become somewhat easier over the past year, which could reflect the spate of layoffs at technology companies from late 2022 through the first half of 2023. Smaller shares of respondents than in the previous survey report difficulty hiring for roles such as AI data scientists, data engineers, and data-visualization specialists, though responses suggest that hiring machine learning engineers and AI product owners remains as much of a challenge as in the previous year.

Looking ahead to the next three years, respondents predict that the adoption of AI will reshape many roles in the workforce. Generally, they expect more employees to be reskilled than to be separated. Nearly four in ten respondents reporting AI adoption expect more than 20 percent of their companies’ workforces will be reskilled, whereas 8 percent of respondents say the size of their workforces will decrease by more than 20 percent.

Looking specifically at gen AI’s predicted impact, service operations is the only function in which most respondents expect to see a decrease in workforce size at their organizations. This finding generally aligns with what our recent research  suggests: while the emergence of gen AI increased our estimate of the percentage of worker activities that could be automated (60 to 70 percent, up from 50 percent), this doesn’t necessarily translate into the automation of an entire role.

AI high performers are expected to conduct much higher levels of reskilling than other companies are. Respondents at these organizations are over three times more likely than others to say their organizations will reskill more than 30 percent of their workforces over the next three years as a result of AI adoption.

4. With all eyes on gen AI, AI adoption and impact remain steady

While the use of gen AI tools is spreading rapidly, the survey data doesn’t show that these newer tools are propelling organizations’ overall AI adoption. The share of organizations that have adopted AI overall remains steady, at least for the moment, with 55 percent of respondents reporting that their organizations have adopted AI. Less than a third of respondents continue to say that their organizations have adopted AI in more than one business function, suggesting that AI use remains limited in scope. Product and service development and service operations continue to be the two business functions in which respondents most often report AI adoption, as was true in the previous four surveys. And overall, just 23 percent of respondents say at least 5 percent of their organizations’ EBIT last year was attributable to their use of AI—essentially flat with the previous survey—suggesting there is much more room to capture value.

Organizations continue to see returns in the business areas in which they are using AI, and they plan to increase investment in the years ahead. We see a majority of respondents reporting AI-related revenue increases within each business function using AI. And looking ahead, more than two-thirds expect their organizations to increase their AI investment over the next three years.

The online survey was in the field April 11 to 21, 2023, and garnered responses from 1,684 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 913 said their organizations had adopted AI in at least one function and were asked questions about their organizations’ AI use. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

The survey content and analysis were developed by Michael Chui , a partner at the McKinsey Global Institute and a partner in McKinsey’s Bay Area office, where Lareina Yee is a senior partner; Bryce Hall , an associate partner in the Washington, DC, office; and senior partners Alex Singla and Alexander Sukharevsky , global leaders of QuantumBlack, AI by McKinsey, based in the Chicago and London offices, respectively.

They wish to thank Shivani Gupta, Abhisek Jena, Begum Ortaoglu, Barr Seitz, and Li Zhang for their contributions to this work.

This article was edited by Heather Hanselman, an editor in the Atlanta office.

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