Deep Model-Based Architectures for Inverse Problems Under Mismatched Priors-Reference-Cited by-同舟云学术

Deep Model-Based Architectures for Inverse Problems Under Mismatched Priors

Published:2022-09 Issue:3 Volume:3 Page:468-480
ISSN:2641-8770
Container-title:IEEE Journal on Selected Areas in Information Theory
language:
Short-container-title:IEEE J. Sel. Areas Inf. Theory

Author:

Shoushtari Shirin¹^ORCID,Liu Jiaming¹^ORCID,Hu Yuyang¹^ORCID,Kamilov Ulugbek S.²^ORCID

Affiliation:

1. Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA

2. Department of Computer Science and Engineering and the Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA

Funder

NSF CAREER Award

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Medicine

Link

https://ieeexplore.ieee.org/ielam/8700143/10077203/9940488-aam.pdf

Reference84 articles.

1. Plug-and-Play priors for model based reconstruction

2. Model Adaptation for Inverse Problems in Imaging

3. Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging: Theory, algorithms, and applications

4. Deep equilibrium models;bai;Proc Adv Neural Inf Process Syst,2019

5. Learning fast approximation of sparse coding;gregor;Proc 27th Int Conf Mach Learn,2010

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