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 the Other section.
P/s: If you know any source code please let me know.
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TGDOF [Code][Matlab]
- R. Liu, Y. ZHang, S. Cheng, X. Fan, Z. Luo, "A theoretically guaranteed optimization framework for robust compressive sensing MRI," Proceeding of the AAAI Conference on Artifical Intelligence, 2019.
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DNN-CS-STM32-MCU [Code] [Tensorflow]
- Lab. of Statistical Signal Processing - Deep Neural Network for CS based signal reconstruction on STM32 MCU board
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TIP-CSNet [DOI] [Code][Matconvnet]
- W. Shi et al., Image Compressed Sensing using Convolutional Neural Network, IEEE Trans. Image Process, 2019.
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Perceptual-CS [[Code]] (https://github.com/jiang-du/Perceptual-CS) [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.
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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.
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CSNet [Code] [Code-ReImp] [PDF] [DOI] [Matconvnet] [Code-ReImp-Pytorch]
- W. Shi, F. Jaing, S. Zhang, and D. Zhao, "Deep networks for compressed image sensing", IEEE International Conference on Multimedia and Expo (ICME), 2017.
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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.
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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.
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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.
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- S. Schneider, "A deep learning approach to compressive sensing with convolutional autoencoders," tech. report, 2016.
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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.
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Scalable Compressed Sensing Network (SCSNet) [DOI] [PDF] [Code][Matconvnet]
- W. Shi et al., Scalable Convolutional Neural Network for Image Compressed Sensing, CVPR 2019.
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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.
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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.
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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.
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- K. Xu, Z. Zhang, and F. Ren, "LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing Reconstruction," arXiv:1807.09388.
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- Thuong Nguyen Canh and Hajime Nagahara, "Deep Compressive Sensing for Visual Privacy Protection in FlatCam Imaging," IEEE the International Conference on Computer Vision Workshop, 2019.)
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Tensor-ADMM-Net-CSI[Code] [Tensorflow]
- Jiawei Ma, Xiao-Yang Liu, Zheng Shou, Xin Yuan, "Deep Tensor ADMM-Net for Snapshot Compressive Imaging," IEEE ICCV, Nov. 2019.
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ADMM-CSNet[Code]
- 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, 2019.
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DCS-GAN [Code][Pdf] - Available Soon from DeepMind
- Yan Wu, Mihaela Rosca, Timothy Lillicrap, Deep Compressive Sensing, Arxiv 2019.
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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.
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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.
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- 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.
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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.
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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.
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- M. Seitzer et al., "Adversarial and Perceptual Refinement Compressed Sensing MRI Reconstruction," MICCAI 2018.
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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
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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.
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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.
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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.
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- Qiegen Liu and Henry Leung, Synthesis-analysis deconvolutional network for compressed sensing, IEEE International Conference on Image Processing, 2017.
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CSGM [Code] [PDF] [Tensorflow]
- A. Bora, A. Jalal, A. G. Dimakis, "Compressed sensing using Generative Models," arXiv:1703.03208, 2017.
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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.
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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.
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GANCS [Code] [PDF] [Tensorflow]
- M. Mardani et al., "Compressed Sensing MRI based on Deep Generative Adversarial Network", arXiv:1706.00051, 2017.
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CSNN [Code] [DOI] [Matlab][Tensorflow]
- Yonar and Lee et. al., A Compressed Sensing Framework for Efficient Dissection of Neural Circuits." (2019) Nature Methods 16, pages126–133.
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- Abdelrahman Taha, Muhammad Alrabeiah, Ahmed Alkhateeb, "Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning," arXiv:1904.10136, Apr 2019.
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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.
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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
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Super-LiDAR [Code] [PDF] [Tensorflow]
- Nathaniel Chodosh, Chaoyang Wang, Simon Lucey, "Deep Convolutional Compressed Sensing for LiDAR Depth Completion," arXiv:1803.08949, 2018.
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Unpaired-GANCS [Code] [Tensorflow]
- Reconstruct under sampled MRI image
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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
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DL-CSI [Code] [PDF] [Tensorflow][Keras
- Chao-Kai Wen, Wan-Ting Shih, and Shi Jin, “Deep learning for massive MIMO CSI feedback,” IEEE Wireless Communications Letters, 2018.
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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.
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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
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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.