Skip to content
Collection of reproducible deep learning for compressive sensing
Branch: master
Clone or download
Type Name Latest commit message Commit time
Failed to load latest commit information. update paper from DeepMind Jun 9, 2019

Reproducible Deep Compressive Sensing

Collection of source code for deep learning-based compressive sensing (DCS). Links for source code, pdf, doi are available. Related works are classified based on the sampling matrix type (frame-based/block-based), sampling scale (single scale, multi-scale), and deep learning platform.

Code for other than sampling, reconstruction of image/video are given in Other section.

P/s: If you know any source code please let me know.

Block-based DCS

Single-Scale Sensing

  • Perceptual-CS [Code] [DOI] [Caffe]

    • J. Du, X. Xie, C. Wang, and G. Shi, "Perceptual Compressive Sensing," Chinese Conference on Pattern Recognition and Computer Vision (PRCV), pp. 268 - 279, 2018.
  • ISTA-Net [Code] [PDF] [Tensorflow]

    • Z. Jian and G. Bernard, "ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing", IEEE International Conference on Computer Vision and Pattern Recognition, 2018.
  • CSNet [Code] [Code-ReImp] [PDF] [DOI] [Matconvnet]

    • W. Shi, F. Jaing, S. Zhang, and D. Zhao, "Deep networks for compressed image sensing", IEEE International Conference on Multimedia and Expo (ICME), 2017.
  • DeepInv [Code-ReImp] [PDF] [DOI]

    • A. Mousavi, R. G. Baraniuk et al., "Learning to invert: Signal recovery via Deep Convolutional Networks," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2017.
  • DBCS [Code] [PDF] [DOI] [Matlab]

    • A. Adler, D.Boublil, and M. Zibulevsky, "Block-based compressed sensing of images via deep learning,", IEEE International Workshop on Multimedia Signal Processing (MMSP), 2017.
  • DR2Net [Code] [Code] [PDF] [Caffe]

    • H. Yao, F. Dai, D. Zhang, Y. Ma, S. Zhang, Y. Zhang, and Q. Tian, "DR2-net: Deep residual reconstruction network for image compressive sensing", arXiv:1702.05743, 2017.
  • CS-CAE [Code] [PDF] [Theanos]

    • S. Schneider, "A deep learning approach to compressive sensing with convolutional autoencoders," tech. report, 2016.
  • ReconNet [Code] [Code] [PDF] [DOI] [Caffe]

    • K. Kulkarni, S. Lohi, P. Turaga, R. Kerviche, A. Ashok, "ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements," IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

Multi-Scale Sensing

  • DoC-DCS [Code] [PDF] [MatcovnNet]

    • T. N. Canh and B. Jeon, "Difference of Convolution for Deep Compressive Sensing," IEEE International Conference on Imave Processing (ICIP), 2019.
  • DCSNet [Code] [PDF] [MatcovnNet]

    • T. N. Canh and B. Jeon, "Multi-Scale Deep Compressive Sensing Network," IEEE International Conference on Visual Communication and Imave Processing (VCIP), 2018.
  • MS-CSNet [Code] [DOI] [MatconvNet]

    • W. Shi, F. Jiang, S. Liu, D. Zhao, "Multi-Scale Deep Networks for Image Compressed Sensing," IEEE International Conference on Image Processing (ICIP), 2018.
  • LAPRAN [Code] [PDF] [PyTorch]

    • K. Xu, Z. Zhang, and F. Ren, "LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing Reconstruction," arXiv:1807.09388.

Frame-based DCS

  • DCS-GAN [Code][Pdf] - Available Soon from DeepMind

    • Yan Wu, Mihaela Rosca, Timothy Lillicrap, Deep Compressive Sensing, Arxiv 2019.
  • F-CSRG [Code] [PDF] [Tensorflow]

    • Shaojie Xu, Sihan Zeng, Justin Romberg, "Fast Compressive Sensing Recovery Using Generative Models with Structured Latent Variables ," arXiv:1806.10175, 2019.
  • L1AE [Code] [PDF] [Tensorflow]

    • Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi, Felix X. Yu, Daniel Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar, "Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling," arXiv:1806.10175, 2018.
  • DIP [Code] [PDF] [Torch]

    • David Van Veen; Ajil Jalal, Eric Price; Sriram Vishwanath; Alexandros G. Dimakis, "Compressed Sensing with Deep Image Prior and Learned Regularization," arXiv:1806.06438, 2018.
  • Deep-ADMM-Net [Code] [DOI] [MatconvNet]

    • Yan Yang ; Jian Sun ; HUIBIN LI ; Zongben Xu, "ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing," IEEE Transaction on Pattern Recognition and Machine Intelligence, 2018.
  • VAR-MSI [Code] [[PDF]] [DOI] [Tensorflow]

    • H. Kerstin et al., "Learning a variational network for reconstruction of accelerated MRI data," Magnetic Resonance in Medicine, vol. 79, no. 6, 2018.
  • CSMRI [Code] [PDF] [PyTorch]

    • M. Seitzer et al., "Adversarial and Perceptual Refinement Compressed Sensing MRI Reconstruction," MICCAI 2018.
  • KCS-Net [Code] [PDF] [MatconvNet]

    • T. N. Canh and B. Jeon, "Deep Learning-Based Kronecker Compressive Imaging", IEEE International Conference on Consumer Electronic Asia, 2018
  • DAGAN [Code] [PDF] [DOI] [Tensorflow]

    • G. Yang et al., "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction," IEEE Transaction on Medical Imaging, vol. 37, no. 6, 2018.
  • DeepVideoCS [Web] [Code] [PDF] [DOI] [PyTorch]

    • M. Illiasdis, L. Spinoulas, A. K. Katsaggelos, "Deep fully-connected networks for video compressive sensing," Elsevier Digital Signal Processing, vol. 72, 2018.
  • CSVideoNet [Code] [PDF] [Caffe] [Matlab]

    • K. Xu and F. Ren, "SVideoNet: A Recurrent Convolutional Neural Network for Compressive Sensing Video Reconstruction," arXiv:162.05203, 2018.
  • SADN [Code][Doi] [Matlab]

    • Qiegen Liu and Henry Leung, Synthesis-analysis deconvolutional network for compressed sensing, IEEE International Conference on Image Processing, 2017.
  • CSGM [Code] [PDF] [Tensorflow]

    • A. Bora, A. Jalal, A. G. Dimakis, "Compressed sensing using Generative Models," arXiv:1703.03208, 2017.
  • Learned D-AMP [Code] [PDF] [Tensorflow]

    • C. A. Metzler et al., "Learned D-AMP: Principled Neural Network based Compressive Image Recovery," Advances in Neural Information Processing Systems, 2017.
  • Deep-Ternary [Code] [PDF] [Tensorflow]

    • D. M. Nguyen, E. Tsiligianni and N. Deligiannis, "Deep learning sparse ternary projections for compressed sensing of images," IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2017.
  • GANCS [Code] [PDF] [Tensorflow]

    • M. Mardani et al., "Compressed Sensing MRI based on Deep Generative Adversarial Network", arXiv:1706.00051, 2017.


  • LIS-DL [Code] [PDF] [Matlab]

    • Abdelrahman Taha, Muhammad Alrabeiah, Ahmed Alkhateeb, "Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning," arXiv:1904.10136, Apr 2019.
  • VAE-GANs [Code] [PDF] [Python]

    • Vineet Edupuganti, Morteza Mardani, Joseph Cheng, Shreyas Vasanawala, John Pauly, "VAE-GANs for Probabilistic Compressive Image Recovery: Uncertainty Analysis," arxiv1901.1128, 2019.
  • Sparse-Gen [Code] [[PDF] [Tensorflow]

    • Manik Dhar, Aditya Grover, Stefano Ermon, "Modeling Sparse Deviations for Compressed Sensing using Generative Models," International Conference on Machine Learning (ICML), 2018
  • Super-LiDAR [Code] [PDF] [Tensorflow]

    • Nathaniel Chodosh, Chaoyang Wang, Simon Lucey, "Deep Convolutional Compressed Sensing for LiDAR Depth Completion," arXiv:1803.08949, 2018.
  • Unpaired-GANCS [Code] [Tensorflow]

    • Reconstruct under sampled MRI image
  • CSGAN [Code] [PDF] [Tensorflow]

    • M. Kabkab, P. Samangouei, and R. Chellappa, "Task-Aware Compressed Sensing with Generative Adversarial Networks," AAAI Conference on Artificial Intelligence, 2018
  • US-CS [Code] [PDF] [DOI] [Tensorflow]

    • D. Perdios, A. Besson, M. Arditi, and J. Thiran, "A Deep Learning Approach to Ultrasound Image Recovery", IEEE International Ultranosics Symposium, 2017.
  • DeepIoT [Code-ReImplement] [PDF] [Tensorflow]

    • Shuochao Yao, Yiran Zhao, Aston Zhang, Lu Su, Tarek Abdelzaher, "DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework," AAAI Conference on Artificial Intelligence, 2018
  • LSTM_CS [Code] [PDF] [DOI] [Matlab]

    • H. Palangi, R. Ward, and L. Deng, "Distributed Compressive Sensing: A Deep Learning Approach," IEEE Transaction on Signal Processing, vol. 64, no. 17, 2016.
You can’t perform that action at this time.