Learning to Invert: Signal Recovery via Deep Convolutional Networks

14 January 2017

Richard G. Baraniuk

Papers citing "Learning to Invert: Signal Recovery via Deep Convolutional Networks"

27 / 27 papers shown

Title
Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Eric Chen Xi Chen A. Maleki S. Jalali 38 0 0 08 Jan 2025
Deep Physics-Guided Unrolling Generalization for Compressed Sensing Bin Chen Jie Song Jingfen Xie Jian Zhang AI4CE 22 14 0 18 Jul 2023
Content-aware Scalable Deep Compressed Sensing Bin Chen Jian Zhang 27 55 0 19 Jul 2022
Deep Coding Patterns Design for Compressive Near-Infrared Spectral Classification Jorge Bacca Alejandra Hernandez-Rojas Henry Arguello 12 2 0 27 May 2022
Survey of Deep Learning Methods for Inverse Problems S. Kamyab Zihreh Azimifar Rasool Sabzi Paul Fieguth 21 3 0 07 Nov 2021
Jointly Sparse Signal Recovery and Support Recovery via Deep Learning with Applications in MIMO-based Grant-Free Random Access Ying Cui Shuaichao Li Wanqing Zhang 24 62 0 05 Aug 2020
FLOT: Scene Flow on Point Clouds Guided by Optimal Transport Gilles Puy Alexandre Boulch Renaud Marlet 3DPC OT 137 183 0 22 Jul 2020
Image Deconvolution via Noise-Tolerant Self-Supervised Inversion H. Kobayashi A. Solak Joshua D. Batson Loic A. Royer 16 13 0 11 Jun 2020
Deep Learning Techniques for Inverse Problems in Imaging Greg Ongie A. Jalal Christopher A. Metzler Richard G. Baraniuk A. Dimakis Rebecca Willett 13 521 0 12 May 2020
Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging Xin Yuan Yang Liu J. Suo Qionghai Dai 21 178 0 30 Mar 2020
Inverse Problems, Deep Learning, and Symmetry Breaking Kshitij Tayal Chieh-Hsin Lai Vipin Kumar Ju Sun AI4CE 72 15 0 20 Mar 2020
Multi-Channel Deep Networks for Block-Based Image Compressive Sensing Siwang Zhou Yan He Yonghe Liu Chengqing Li Jianming Zhang 26 113 0 28 Aug 2019
Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging Iris A. M. Huijben Bastiaan S. Veeling K. Janse M. Mischi Ruud J. G. van Sloun 27 51 0 15 Aug 2019
Deep Representation with ReLU Neural Networks Andreas Heinecke W. Hwang 39 0 0 29 Mar 2019
Shallow Neural Networks for Fluid Flow Reconstruction with Limited Sensors N. Benjamin Erichson L. Mathelin Z. Yao Steven L. Brunton Michael W. Mahoney J. Nathan Kutz AI4CE 27 34 0 20 Feb 2019
Algorithmic Aspects of Inverse Problems Using Generative Models C. Hegde GAN 25 21 0 08 Oct 2018
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling Shanshan Wu A. Dimakis Sujay Sanghavi Felix X. Yu D. Holtmann-Rice Dmitry Storcheus Afshin Rostamizadeh Sanjiv Kumar SSL 23 53 0 26 Jun 2018
Learning to Detect N. Samuel Tzvi Diskin A. Wiesel 19 431 0 19 May 2018
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search Masanori Suganuma Mete Ozay Takayuki Okatani 19 87 0 01 Mar 2018
Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guarantees Viraj Shah C. Hegde GAN 21 162 0 23 Feb 2018
Full Image Recover for Block-Based Compressive Sensing Xuemei Xie Chenye Wang Jiang Du Guangming Shi 35 16 0 01 Feb 2018
MoDL: Model Based Deep Learning Architecture for Inverse Problems H. Aggarwal M. Mani M. Jacob 81 999 0 07 Dec 2017
Fully Convolutional Measurement Network for Compressive Sensing Image Reconstruction Jiang Du Xuemei Xie Chenye Wang Guangming Shi Xun Xu Yuxiang Wang 35 61 0 21 Nov 2017
CNN-Based Projected Gradient Descent for Consistent Image Reconstruction Harshit Gupta Kyong Hwan Jin H. Nguyen Michael T. McCann M. Unser 3DV 35 361 0 06 Sep 2017
Convolutional Neural Networks for Non-iterative Reconstruction of Compressively Sensed Images Suhas Lohit K. Kulkarni Ronan Kerviche Pavan Turaga A. Ashok 9 114 0 15 Aug 2017
Deep Learning-Based Communication Over the Air Sebastian Dörner Sebastian Cammerer J. Hoydis S. Brink 24 703 0 11 Jul 2017
An Introduction to Deep Learning for the Physical Layer Tim O'Shea J. Hoydis AI4CE 89 2,172 0 02 Feb 2017