Deep Learning for Linear Inverse Problems Using the Plug-and-Play Priors Framework

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp39728.2021.9413947 ◽

2021 ◽

Author(s):

Wei Chen ◽

David Wipf ◽

Miguel Rodrigues

Keyword(s):

Deep Learning ◽

Inverse Problems ◽

Plug And Play ◽

Linear Inverse Problems

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Overcoming Measurement Inconsistency In Deep Learning For Linear Inverse Problems: Applications In Medical Imaging

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp39728.2021.9414173 ◽

2021 ◽

Author(s):

Marija Vella ◽

Joao F. C. Mota

Keyword(s):

Deep Learning ◽

Medical Imaging ◽

Inverse Problems ◽

Linear Inverse Problems

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Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth

IEEE Journal on Selected Areas in Communications ◽

10.1109/jsac.2020.3036959 ◽

2021 ◽

Vol 39 (1) ◽

pp. 4-17

Author(s):

Wei Chen ◽

Bowen Zhang ◽

Shi Jin ◽

Bo Ai ◽

Zhangdui Zhong

Keyword(s):

Deep Learning ◽

Inverse Problems ◽

Communication Systems ◽

Learning Approach ◽

Linear Inverse Problems

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Model-based Bayesian deep learning architecture for linear inverse problems in computational imaging

Electronic Imaging ◽

10.2352/issn.2470-1173.2021.15.coimg-201 ◽

2021 ◽

Keyword(s):

Deep Learning ◽

Inverse Problems ◽

International Symposium ◽

Fast Track ◽

Computational Imaging ◽

Linear Inverse Problems ◽

Model Based ◽

Electronic Imaging

Fast track article for IS&T International Symposium on Electronic Imaging 2021: Computational Imaging XIX proceedings.

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Deep learning methods for solving linear inverse problems: Research directions and paradigms

Signal Processing ◽

10.1016/j.sigpro.2020.107729 ◽

2020 ◽

Vol 177 ◽

pp. 107729 ◽

Cited By ~ 1

Author(s):

Yanna Bai ◽

Wei Chen ◽

Jie Chen ◽

Weisi Guo

Keyword(s):

Deep Learning ◽

Inverse Problems ◽

Learning Methods ◽

Research Directions ◽

Linear Inverse Problems

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Onsager-corrected deep learning for sparse linear inverse problems

2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP) ◽

10.1109/globalsip.2016.7905837 ◽

2016 ◽

Cited By ~ 32

Author(s):

Mark Borgerding ◽

Philip Schniter

Keyword(s):

Deep Learning ◽

Inverse Problems ◽

Linear Inverse Problems

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Deep learning-based solvability of underdetermined inverse problems in medical imaging

Medical Image Analysis ◽

10.1016/j.media.2021.101967 ◽

2021 ◽

Vol 69 ◽

pp. 101967

Author(s):

Chang Min Hyun ◽

Seong Hyeon Baek ◽

Mingyu Lee ◽

Sung Min Lee ◽

Jin Keun Seo

Keyword(s):

Deep Learning ◽

Medical Imaging ◽

Inverse Problems

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Solving Fourier Phase Retrieval with a Reference Image as a Sequence of Linear Inverse Problems

10.1109/icip42928.2021.9506095 ◽

2021 ◽

Author(s):

Fahimeh Arab ◽

M. Salman Asif

Keyword(s):

Inverse Problems ◽

Phase Retrieval ◽

Reference Image ◽

Linear Inverse Problems

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Loaded differential equations and linear inverse problems for elliptic equations

Complex Variables and Elliptic Equations ◽

10.1080/17476933.2020.1793970 ◽

2020 ◽

pp. 1-19

Author(s):

A.I. Kozhanov ◽

T. N. Shipina

Keyword(s):

Inverse Problems ◽

Differential Equations ◽

Elliptic Equations ◽

Linear Inverse Problems

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A fast algorithm for the constrained formulation of compressive image reconstruction and other linear inverse problems

2010 IEEE International Conference on Acoustics, Speech and Signal Processing ◽

10.1109/icassp.2010.5495758 ◽

2010 ◽

Cited By ~ 12

Author(s):

Manya V. Afonso ◽

Jose M. Bioucas-Dias ◽

Mario A. T. Figueiredo

Keyword(s):

Image Reconstruction ◽

Inverse Problems ◽

Fast Algorithm ◽

Linear Inverse Problems

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Deriving algorithms for computing sparse solutions to linear inverse problems

Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136) ◽

10.1109/acssc.1997.680585 ◽

2002 ◽

Cited By ~ 11

Author(s):

B.D. Rao ◽

K. Kreutz-Delgado

Keyword(s):

Inverse Problems ◽

Linear Inverse Problems ◽

Sparse Solutions

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