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DTSTART:20250101T000000
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DTSTART;TZID=Asia/Kolkata:20250227T140000
DTEND;TZID=Asia/Kolkata:20250227T160000
DTSTAMP:20260525T170550
CREATED:20250224T093957Z
LAST-MODIFIED:20250224T093957Z
UID:241962-1740664800-1740672000@ee.iisc.ac.in
SUMMARY:EE  PhD  Defense: Infimal Convolution Based Regularization   for Image recovery
DESCRIPTION:Student : Deepak G Skariah \nAdvisor : Prof. Muthuvel Arigovindan \nTitle :  Infimal Convolution Based Regularization   for Image recovery \nDate and Time:   27.02.2025 (Thursday)\,  2 pm. \nVenue :  MMCR\, Department of Electrical Engineering \n Meeting link \nThesis examiners:   Prof.  Kedar Khare\,  Prof. Naren Nayak \nDefense examiner:   Prof.  Kedar Khare \nAbstract\nThe quality of image captured by acquisition devices has increased drastically over the years largely due to a revolution in imaging sensor capability. But\, image acquisition under low illumination continues to be a bottleneck for imaging devices such as  optical microscopes   leading to blurred and noisy images.  A potential solution to this limitation   is a computational approach known as image restoration. An image restoration   algorithm recovers  an estimate of the original image from a noisy blurred observation  while assuming a knowledge of the image degradation model.  The restoration problem is even more challenging when it comes to a spatio-temporal signal as a good restoration scheme needs to be mindful of presence of motion in the measured signal. This means that in spatio-temporal signal restoration problem\, the algorithm should ensure temporal regularity of restored signal in addition to spatial regularity. Regularization based image restoration attempts to pose image restoration problem as a regularized optimization problem from the measured signal.  We propose to exploit the concept of infimal convolution from convex analysis to design effective and efficient restoration schemes for images and spatio-temporal images. \nIn our first work\, we address the problem of regularization design. We   propose  a family of derivative based regularization which we call generalized unitary invariant regularization and it belongs to class of infimal convolution based functionals. We  also design an algorithmic scheme to optimize the resultant optimization problem. We demonstrate the quality of proposed algorithm and restoration scheme through multiple experiments on simulated data. \nIn our  second work\, we address the restoration of spatio-temporal images measured from TIRF microscopes where a sequence of noisy blurred images are observed over time. We once again exploit the infimal convolution based approach to design a novel spatio-temporal regularizer that is tailor made for above class of signals. The proposed regularization was designed to ensure both  spatial and temporal regularity of restored signal. The resultant regularization functional is defined as an optimization problem where the cost is a weighted sum of two constituent functions where the two functions play the role of promoting spatial and temporal regularity respectively.   We also design an algorithm to optimize the resultant restoration problem using this regularization. We demonstrate the quality of the proposed algorithm by testing the restoration quality against spatio-temporal measurements    collected from TIRF microscopes. \nIn the third and final work we address the problem of estimating the relative weights in spatio-temporal regularization functional designed based on infimal convolution formulation. We propose a renewed optimization model where the spatio-temporal signal is estimated together with the better quality image estimate by incorporating the weights as part  of the optimization problem. We also design an iterative scheme to optimize the resultant joint optimization model. We demonstrate the effectiveness of this scheme against other  joint optimization schemes for spatio-temporal signal estimation.
URL:https://ee.iisc.ac.in/event/ee-phd-defense-infimal-convolution-based-regularization-for-image-recovery/
LOCATION:Multi-Media Class Room (MMCR)\, EE Department (Hybrid mode)
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