Content Gazette - December 2020 - 1

Webinar Details

Date: 25 August 2020
Time: 11:00 am EDT (New York time)
Speaker:
Dr. Saiprasad Ravishankar
Register for the Webinar
Duration: Approximately 1 hour

The IEEE Signal Processing Society would like to express our concern and support for the
members of our global community and all affected by the current COVID-19 pandemic. We
appreciate your continued patience and support as we work together to navigate these
unforeseen and uncertain circumstances. We hope that you, your families, and your communities
are safe!
Date: 25 August 2020
Speaker: Saiprasad Ravishankar, Michigan State University
Title: From Transform Learning to Deep Learning and Beyond for Imaging
Abstract: The next generation computational imaging systems are expected to be increasingly
data-driven. There is growing interest in learning effective models for image reconstruction from
limited training data, which would benefit many applications. In this talk, we will first review
efficient methods for learning sparsifying transform models for signals along with convergence
analysis. Various properties can be enforced during transform learning such as sparsity in a union
of transforms with clustering, non-local sparsity, or multi-layer models, which help improve image
reconstruction quality over conventional schemes in applications such as X-ray computed
tomography (CT) or magnetic resonance imaging. Sparsifying transforms are often learned in an
unsupervised manner from (unpaired) images or on-the-fly from measurements and used to aid
model-based image reconstruction that combines physics-based forward models, noise models,
and image priors. We show that these sparsity-based models can also be trained in a supervised
manner for optimal reconstruction quality by learning the model parameters from (deep) unrolled
block coordinate descent algorithms for transform-regularized problems, or by directly learning
the sparsity regularizer itself within model-based reconstruction formulations. The latter approach
leads to a challenging bilevel training optimization problem for which we analyze the optimization
landscape with l1 sparsity and propose new algorithms without approximations or relaxations.
Our initial results show promise for such supervised operator learning in denoising compared to
popular methods. We then propose unified bilevel machine learning formulations that effectively
combine the benefits of imaging forward models, noise models, supervised deep learning,
unsupervised learning-based models, and analytical priors. Our unified approach substantially
improves image reconstruction quality in low-dose X-ray CT compared to popular recent
methods. We conclude with a brief discussion of ongoing and future research pathways.
About the Presenter:

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DECEMBER 2020
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