REST: Robust lEarned Shrinkage: Thresholding Network Taming Inverse Problems with Model Mismatch

Wei Pu, Chao Zhou, Yonina C Eldar, Miguel R. D Rodrigues

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider compressive sensing problems with model mismatch where one wishes to recover a sparse high-dimensional vector from low-dimensional observations subject to uncertainty in the measurement operator. In particular, we design a new robust deep neural network architecture by applying algorithm unfolding techniques to a robust version of the underlying recovery problem. Our proposed network -named Robust lErned Shrinkage-Thresholding (REST) -exhibits additional features including enlarged number of parameters and normalization processing compared to state-of-the-art deep architecture Learned Iterative Shrinkage-Thresholding Algorithm (LISTA), leading to the reliable recovery of the signal under sample-wise varying model mismatch. Our proposed network is also shown to outperform LISTA in compressive sensing problems under sample-wise varying model mismatch.
Original languageEnglish
Title of host publicationICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Pages2885-2889
Number of pages5
ISBN (Electronic)9781728176055
DOIs
StatePublished - 13 May 2021
Event2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Toronto, ON, Canada
Duration: 6 Jun 202111 Jun 2021

Publication series

NameICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ISSN (Print)2379-190X

Conference

Conference2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Period6/06/2111/06/21

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