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Neural Proximal Gradient Descent for Compressive Imaging

Neural Proximal Gradient Descent for Compressive Imaging

1 June 2018
Morteza Mardani
Qingyun Sun
Shreyas S. Vasawanala
Vardan Papyan
Hatef Monajemi
John M. Pauly
D. Donoho
ArXiv (abs)PDFHTML

Papers citing "Neural Proximal Gradient Descent for Compressive Imaging"

50 / 72 papers shown
Title
Sparsity-Driven Parallel Imaging Consistency for Improved Self-Supervised MRI Reconstruction
Sparsity-Driven Parallel Imaging Consistency for Improved Self-Supervised MRI Reconstruction
Yasar Utku Alçalar
Mehmet Akçakaya
29
1
0
30 May 2025
Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems
Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems
Eric Chen
Xi Chen
A. Maleki
S. Jalali
155
0
0
08 Jan 2025
Injectivity capacity of ReLU gates
Injectivity capacity of ReLU gates
Mihailo Stojnic
70
0
0
28 Oct 2024
A Unified Model for Compressed Sensing MRI Across Undersampling Patterns
A Unified Model for Compressed Sensing MRI Across Undersampling Patterns
Armeet Singh Jatyani
Jiayun Wang
Zihui Wu
Miguel Liu-Schiaffini
Bahareh Tolooshams
Anima Anandkumar
Anima Anandkumar
77
2
0
05 Oct 2024
Controlled Learning of Pointwise Nonlinearities in Neural-Network-Like Architectures
Controlled Learning of Pointwise Nonlinearities in Neural-Network-Like Architectures
Michael Unser
Alexis Goujon
Stanislas Ducotterd
123
2
0
23 Aug 2024
Learning Regularization for Graph Inverse Problems
Learning Regularization for Graph Inverse Problems
Moshe Eliasof
Md Shahriar Rahim Siddiqui
Carola-Bibiane Schönlieb
Eldad Haber
GNN
195
0
0
19 Aug 2024
Graph Neural Reaction Diffusion Models
Graph Neural Reaction Diffusion Models
Moshe Eliasof
Eldad Haber
Eran Treister
DiffMAI4CE
98
3
0
16 Jun 2024
A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction
  Using Deep Learning
A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learning
Wanyu Bian
119
7
0
03 Jun 2024
A General Continuous-Time Formulation of Stochastic ADMM and Its
  Variants
A General Continuous-Time Formulation of Stochastic ADMM and Its Variants
Chris Junchi Li
71
0
0
22 Apr 2024
Robustness and Exploration of Variational and Machine Learning
  Approaches to Inverse Problems: An Overview
Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview
Alexander Auras
Kanchana Vaishnavi Gandikota
Hannah Droege
Michael Moeller
AAML
72
0
0
19 Feb 2024
An Over Complete Deep Learning Method for Inverse Problems
An Over Complete Deep Learning Method for Inverse Problems
Moshe Eliasof
Eldad Haber
Eran Treister
54
3
0
07 Feb 2024
What's in a Prior? Learned Proximal Networks for Inverse Problems
What's in a Prior? Learned Proximal Networks for Inverse Problems
Zhenghan Fang
Sam Buchanan
Jeremias Sulam
90
12
0
22 Oct 2023
Deep Unrolling Networks with Recurrent Momentum Acceleration for
  Nonlinear Inverse Problems
Deep Unrolling Networks with Recurrent Momentum Acceleration for Nonlinear Inverse Problems
Qingping Zhou
Jiayu Qian
Junqi Tang
Jinglai Li
AI4CE
66
5
0
30 Jul 2023
Feature Transportation Improves Graph Neural Networks
Feature Transportation Improves Graph Neural Networks
Moshe Eliasof
E. Haber
Eran Treister
GNN
109
12
0
29 Jul 2023
Towards Constituting Mathematical Structures for Learning to Optimize
Towards Constituting Mathematical Structures for Learning to Optimize
Jialin Liu
Xiaohan Chen
Zhangyang Wang
W. Yin
HanQin Cai
106
13
0
29 May 2023
DRIP: Deep Regularizers for Inverse Problems
DRIP: Deep Regularizers for Inverse Problems
Moshe Eliasof
E. Haber
Eran Treister
90
8
0
30 Mar 2023
Learning Trees of $\ell_0$-Minimization Problems
Learning Trees of ℓ0\ell_0ℓ0​-Minimization Problems
G. Welper
64
0
0
06 Feb 2023
Estimating a potential without the agony of the partition function
Estimating a potential without the agony of the partition function
E. Haber
Moshe Eliasof
L. Tenorio
61
2
0
19 Aug 2022
GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction
GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction
Batu Mehmet Ozturkler
Arda Sahiner
Tolga Ergen
Arjun D Desai
Christopher M. Sandino
S. Vasanawala
John M. Pauly
Morteza Mardani
Mert Pilanci
50
4
0
18 Jul 2022
Uncertainty Quantification for Deep Unrolling-Based Computational
  Imaging
Uncertainty Quantification for Deep Unrolling-Based Computational Imaging
Canberk Ekmekci
Müjdat Çetin
UQCV
107
12
0
02 Jul 2022
RU-Net: Regularized Unrolling Network for Scene Graph Generation
RU-Net: Regularized Unrolling Network for Scene Graph Generation
Xin Lin
Changxing Ding
Jing Zhang
Yibing Zhan
Dacheng Tao
81
34
0
03 May 2022
PnP-ReG: Learned Regularizing Gradient for Plug-and-Play Gradient
  Descent
PnP-ReG: Learned Regularizing Gradient for Plug-and-Play Gradient Descent
Rita Fermanian
Mikael Le Pendu
C. Guillemot
73
7
0
29 Apr 2022
Scale-Equivariant Unrolled Neural Networks for Data-Efficient
  Accelerated MRI Reconstruction
Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction
Beliz Gunel
Arda Sahiner
Arjun D Desai
Akshay S. Chaudhari
S. Vasanawala
Mert Pilanci
John M. Pauly
MedIm
68
8
0
21 Apr 2022
Physics-Driven Deep Learning for Computational Magnetic Resonance
  Imaging
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging
Kerstin Hammernik
Thomas Kustner
Burhaneddin Yaman
Zhengnan Huang
Daniel Rueckert
Florian Knoll
Mehmet Akçakaya
PINNMedImAI4CE
94
75
0
23 Mar 2022
Learning A 3D-CNN and Transformer Prior for Hyperspectral Image
  Super-Resolution
Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution
Qing Ma
Junjun Jiang
Xianming Liu
Jiayi Ma
ViT
116
59
0
27 Nov 2021
Preconditioned Plug-and-Play ADMM with Locally Adjustable Denoiser for
  Image Restoration
Preconditioned Plug-and-Play ADMM with Locally Adjustable Denoiser for Image Restoration
Mikael Le Pendu
C. Guillemot
72
17
0
01 Oct 2021
A review and experimental evaluation of deep learning methods for MRI
  reconstruction
A review and experimental evaluation of deep learning methods for MRI reconstruction
Arghya Pal
Yogesh Rathi
3DV
111
47
0
17 Sep 2021
Feasibility-based Fixed Point Networks
Feasibility-based Fixed Point Networks
Howard Heaton
Samy Wu Fung
A. Gibali
W. Yin
57
26
0
29 Apr 2021
A Design Space Study for LISTA and Beyond
A Design Space Study for LISTA and Beyond
Tianjian Meng
Xiaohan Chen
Yi Ding
Zhangyang Wang
72
3
0
08 Apr 2021
Equivariant Imaging: Learning Beyond the Range Space
Equivariant Imaging: Learning Beyond the Range Space
Dongdong Chen
Julián Tachella
Mike E. Davies
SSL
110
103
0
26 Mar 2021
Learning to Optimize: A Primer and A Benchmark
Learning to Optimize: A Primer and A Benchmark
Tianlong Chen
Xiaohan Chen
Wuyang Chen
Howard Heaton
Jialin Liu
Zhangyang Wang
W. Yin
266
237
0
23 Mar 2021
Improving Generalizability in Limited-Angle CT Reconstruction with
  Sinogram Extrapolation
Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation
Ce Wang
Haimiao Zhang
Qian Li
Kun Shang
Y. Lyu
Bin Dong
S. Kevin Zhou
130
14
0
09 Mar 2021
Deep Equilibrium Architectures for Inverse Problems in Imaging
Deep Equilibrium Architectures for Inverse Problems in Imaging
Davis Gilton
Greg Ongie
Rebecca Willett
103
183
0
16 Feb 2021
Non-Convex Compressed Sensing with Training Data
Non-Convex Compressed Sensing with Training Data
G. Welper
65
1
0
20 Jan 2021
DAEs for Linear Inverse Problems: Improved Recovery with Provable
  Guarantees
DAEs for Linear Inverse Problems: Improved Recovery with Provable Guarantees
J. Dhaliwal
Kyle Hambrook
118
0
0
13 Jan 2021
Phase Retrieval using Expectation Consistent Signal Recovery Algorithm
  based on Hypernetwork
Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork
Chang-Jen Wang
Chao-Kai Wen
Shang-Ho
S. Tsai
Shi Jin
Geoffrey Ye Li
60
5
0
12 Jan 2021
Convex Regularization Behind Neural Reconstruction
Convex Regularization Behind Neural Reconstruction
Arda Sahiner
Morteza Mardani
Batu Mehmet Ozturkler
Mert Pilanci
John M. Pauly
67
25
0
09 Dec 2020
Model Adaptation for Inverse Problems in Imaging
Model Adaptation for Inverse Problems in Imaging
Davis Gilton
Greg Ongie
Rebecca Willett
OODMedIm
134
49
0
30 Nov 2020
TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to
  Inverse Imaging Problems
TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems
Kaixuan Wei
Angelica Aviles-Rivero
Jingwei Liang
Ying Fu
Hua Huang
Carola-Bibiane Schönlieb
74
35
0
18 Nov 2020
Convergence Acceleration via Chebyshev Step: Plausible Interpretation of
  Deep-Unfolded Gradient Descent
Convergence Acceleration via Chebyshev Step: Plausible Interpretation of Deep-Unfolded Gradient Descent
Satoshi Takabe
Tadashi Wadayama
61
10
0
26 Oct 2020
Unsupervised MRI Reconstruction with Generative Adversarial Networks
Unsupervised MRI Reconstruction with Generative Adversarial Networks
Elizabeth K. Cole
John M. Pauly
S. Vasanawala
Frank Ong
GANMedIm
62
51
0
29 Aug 2020
Neural Network-based Reconstruction in Compressed Sensing MRI Without
  Fully-sampled Training Data
Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data
Alan Q. Wang
Adrian Dalca
M. Sabuncu
91
26
0
29 Jul 2020
FLOT: Scene Flow on Point Clouds Guided by Optimal Transport
FLOT: Scene Flow on Point Clouds Guided by Optimal Transport
Gilles Puy
Alexandre Boulch
Renaud Marlet
3DPCOT
216
187
0
22 Jul 2020
Compressed Sensing via Measurement-Conditional Generative Models
Compressed Sensing via Measurement-Conditional Generative Models
Kyungsu Kim
J. H. Lee
Eunho Yang
GANMedIm
69
3
0
02 Jul 2020
Compressive MR Fingerprinting reconstruction with Neural Proximal
  Gradient iterations
Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations
Dongdong Chen
Mike E. Davies
Mohammad Golbabaee
66
16
0
27 Jun 2020
MetaInv-Net: Meta Inversion Network for Sparse View CT Image
  Reconstruction
MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstruction
Haimiao Zhang
Baodong Liu
Hengyong Yu
Bin Dong
84
63
0
30 May 2020
Deep Learning Techniques for Inverse Problems in Imaging
Deep Learning Techniques for Inverse Problems in Imaging
Greg Ongie
A. Jalal
Christopher A. Metzler
Richard G. Baraniuk
A. Dimakis
Rebecca Willett
128
537
0
12 May 2020
Learning Sampling and Model-Based Signal Recovery for Compressed Sensing
  MRI
Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI
Iris A. M. Huijben
Bastiaan S. Veeling
Ruud J. G. van Sloun
90
33
0
22 Apr 2020
Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI
  Reconstruction
Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI Reconstruction
Elizabeth K. Cole
Joseph Y. Cheng
John M. Pauly
S. Vasanawala
61
11
0
03 Apr 2020
Solving Inverse Problems with a Flow-based Noise Model
Solving Inverse Problems with a Flow-based Noise Model
Jay Whang
Qi Lei
A. Dimakis
132
39
0
18 Mar 2020
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