Trending Topics in Image Processing

Trending Topics in Image Processing

Automatic Image Enhancement and Restoration

During the capture, compression, and transmission of images, the quality of the images is typically compromised. Image blur caused by lens out-of-focus, resolution loss owing to acquisition equipment pixel limitations, high ISO noise spots, and JPEG block artefacts are all common deteriorations. Removing the deterioration while maintaining the original image attributes is all about image enhancement and restoration.

Biomedical Imaging

In the field of biomedical imaging, there is a wide range of imaging modalities available today, including magnetic resonance imaging (MR), CT, PET, OCT, and ultrasound, that may give a wealth of information on the human body's structural and functional processes. There are still numerous fundamental trade-offs between these three features because of operational, budgetary, and physical limits, even though imaging technology has evolved dramatically over the years to enhance resolution and SNR, as well as lower capture speed. Due to issues such as noise, technology-related distortions, low resolution, and contrast, raw data might be totally worthless. Research scientists and physicians typically have difficulty deciphering and analysing biomedical imaging data because of the data's inherent complexity. To help clinicians, radiologists, pathologists, and clinical research scientists better visualize, diagnose, and understand various diseases affecting the human body, researchers in the VIP group are coming up with innovative and exciting solutions to address issues associated with biomedical imaging.

Image Inpainting

Inpainting is the process of filling in the blanks in a picture. Object removal, restoring, transformation, re-targeting, compositing, and image-based rendering are all examples of computer vision problems that rely on these capabilities.

Computer Vision

If you're interested in learning more about computer vision, you're at the right place. Image/video processing, pattern recognition, biological vision, artificial intelligence, and augmented reality are just a few of the topics that fall under the umbrella of computer vision. When it comes to applications, there is a wide variety from simple jobs (such as counting objects on an assembly line) to more difficult duties (such as keeping track of human activity in an outside camera). For example, robot navigation, tracking objects (e.g. tracking vehicles through an intersection), finding specific information (e.g. find all the 'cows' in a large digital image database), recognising events (e.g., did someone leave a suitcase behind at the airport?), creating biological models (e.g., how does the human biological system work?) are all examples of how computer vision can be applied. The VIP lab focuses on computer vision challenges for the majority of its research.

Image Segmentation/Classification

IA digital picture's ability to be mined for information is typically dependent on the process of 'segmentation,' and 'classification', in which the image is broken down into homogeneous sections and the required items are identified. Image processing and pattern recognition algorithms are combined in this key step at computer vision. Color, texture, and form are all examples of what is meant by the term "homogeneous." This approach can be applied in medical imaging, crops in satellite photography, cells in biological tissue, or human faces in conventional digital photographs or video. However, unique items and visuals may necessitate the use of specialized processes to get the best results. The VIP lab's sophisticated work in decoupled active contours serves as a dedicated segmentation example. Automated sea ice categorization is a specific example of classification.

Multiresolution Techniques

In terms of algorithms, models, methodologies, and concepts, multiresolution techniques cover a wide spectrum. It is essential to explicitly represent short-range, mid-range, and long-range interactions in a multiresolution method. One of the most important reasons for using a multiresolution strategy is that:

  • by taking use of long-range phenomena that would otherwise go unappreciated instead of waiting for large-scale convergence of local pixel-level operations, algorithms may work at both fine and coarse scales, which reduces computational complexity.
  • Reducing problem conditioning and increasing numerical resilience, with a multiresolution transformation acting as an algebraic pre-conditioner.
  • By making long-range characteristics available, the process can be simplified by making it easier to deal with than pixel-level features in some cases.
  • When you model or analyse problems on many scales, you gain a greater understanding of the issue at hand.
  • In spite of the fact that several techniques and algorithms have been developed, they generally fall into a few categories:

Wavelet-Based Methods

The decomposition of an image or video into several scales using a wavelet transform is particularly popular for image/video denoising or feeding the coefficients at many scales into a classifier for image classification and segmentation.

Models with a higher level of abstraction (Hierarchical Models)

Scale-independent random fields are expressly used in this model to describe a pixellated, finest-scale random field. Using Markov decomposition methods, the multi-scale model may be simpler in many circumstances. Because it is easier to assert multiple models at different sizes when using a multi-scale model rather than a single scale model that must claim all of the different scale-dependent behaviors simultaneously,

Algorithms with a hierarchical structure (Hierarchical Algorithms)

No matter what kind of model is used, the processing technique can still be considered hierarchical. For example, multigrid methods solve a single-scale linear system by casting the issue onto a hierarchy, while wavelet methods in image processing convert an image into a set of multiscale coefficients within the wavelet domain, where certain operations (like image compression or denoising) are relatively simple.


It is possible to hide secret data in a non-secret file or communication in order to evade discovery; the secret data may then be recovered at its destination. Steganography can be used in conjunction with encryption as an additional layer of protection for data. The Greek terms steganos (meaning concealed or covered) and the Greek root graph combine to form the word steganography (meaning to write).

Remote Sensing

An important aspect of remote sensing is that it allows for frequent monitoring of land, ocean, and atmospheric expanses that any other form of data acquisition cannot obtain. Data produced by remote sensing channels is vast and must be analysed accurately to improve throughput, cut prices, or develop new products. There is currently no known artificial algorithm that can efficiently analyse remotely sensed images. Our remote sensing researchers are working to tackle challenges in denoising, disparate scene registration, multisensor fusion, region segmentation and scene classification using remote sensing. Automated information extraction techniques for understanding remote sensing pictures are the overarching goal of the proposed study.

Scientific Imaging

The terms "image processing" and "computer vision" cover a wide range of applications requiring the use of cameras to collect data. Denoising or compressing photographs of people, buildings, landscapes, and the like is the primary focus of research in this area. It's tough to represent the human environment since it's full of straight lines and edges, thus many of the heuristic approaches for such pictures are used.

Scientists typically use two- or three-dimensional imaging for scientific purposes, typically collected either through a microscope or remotely-sensed pictures captured at a distance. At the micron and kilometer scale, the natural world has a considerably more random, textured or uneven structure that can frequently be described mathematically as a random field. This contrasts with the sophisticated structure of human (meter) size.

Photographic image processing approaches are more heuristic, whereas scientific imaging methods are more model-based. Some of the methods we employ include Markov random fields, Gibbs random fields, hidden Markov models, and wavelet models.

Information Extraction

It's always a challenge to deal with a large volume of text data. Consequently, many businesses and organizations use Information Extraction techniques to automate manual tasks with clever algorithms. It is possible to minimize human labour, cut costs, and make the process less error-prone and more efficient through the use of information extraction.

Stochastic Models

The look of the underlying scene in the data obtained, the placement and trajectory of the interest point, the physical features (e.g., size, shape, colour, etc.) of the objects being detected, etc. are all examples of factors in many image processing, computer vision, and pattern recognition applications that are fraught with uncertainty. A vast range of possible outcomes for each element can't be accounted for efficiently or effectively utilising deterministic methodologies because of the uncertainty inherent in these factors. For a more accurate depiction of the topic at hand, stochastic models allow for the inclusion of such uncertainties. Using unique ways to build stable, large-scale stochastic models, researchers in the VIP lab are working to improve image processing and computer vision challenges, including image denoising and segmentation and registration and classification.

Evolutionary Deep Intelligence

In recent years, deep learning has shown great promise, achieving fantastic achievements and greatly improving the accuracy of a number of tough tasks when compared to previous machine learning approaches. Due to the complexity and size of their computational structures, these systems necessitate high-performance computing (such as supercomputer clusters and GPU arrays). Machine learning expertise are also needed to properly construct and fine-tune huge, complicated structures for deep neural networks. The quest for ever-deeper and more extensive networks to improve cognitive correctness has led to a rise in complexity. Consequently, it has become nearly impossible to use such strong but complicated deep neural networks in settings where computing and energy resources are limited, such as embedded devices, and it has become more difficult to hand-craft such structures.

Discovery Radiomics

Radiomics is bringing a new era of image-driven quantitative tailored cancer decision support and management by extracting and analyzing vast quantities of quantitative characteristics from medical imaging data. In spite of its tremendous potential, radiomics is currently limited by the fact that it relies on actual, predetermined imaging-based feature methods that rely on facets such as intensity, texture, and shape, which can significantly limit its ability to classify the unique traits of different cancer types properly.

Discovery radiomics is the next step in personalised cancer quantification by presenting the concept of forgoing the notion of predetermined feature models by discovering customised, tailored radiomics feature models straight from the amount of medical imaging data that is already out there. Imaging-based features that capture highly unique tumour traits and characteristics beyond what can be captured using pre-defined feature models can now be identified in unprecedented detail, allowing for an unprecedented understanding and characterization of the distinct cancer phenotype associated with different forms of cancer.

Sports Analytics

Data about players, teams, and games may be gleaned from photographs using computer vision techniques known as sports analytics. Do you want to discover how a player's skill level affects the team as a whole? Can a team's chances of reaching the championship game be improved just by playing better defense? These are just a handful of the many questions that sports analytics seeks to solve.

Trending Topics in Image Processing Trending Topics in Image Processing Reviewed by IPR on January 28, 2022 Rating: 5

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