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.
Steganography
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.
Reviewed by IPR
on
January 28, 2022
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