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    压缩感知（Compressed sensing），也被称为压缩采样(Compressive sampling)，稀疏采样(Sparse sampling)，压缩传感。它作为一个新的采样理论，它通过开发信号的稀疏特性，在远小于Nyquist 采样率的条件下，用随机采样获取信号的离散样本，然后通过非线性重建算法完美的重建信号。压缩感知理论一经提出，就引起学术界和工业界的广泛关注。他在信息论、图像处理、地球科学、光学/微波成像、模式识别、无线通信、生物医学工程等领域受到高度关注，并被美国科技评论评为2007年度十大科技进展

    本文整理了基于深度学习的压缩感知（Deep Compressive Sensing，DCS）相关的最新论文、代码实验源代码等资源集合。源代码、pdf、doi等资源都是可用的。相关工作根据采样矩阵类型(基于框架/基于block)、采样规模(single scale、multi-scale)和深度学习平台进行分类。

     

    其他部分给出了除采样、图像/视频重建以外的代码。

 

    本文资源整理自网络，源地址：https://github.com/ngcthuong/Reproducible-Deep-Compressive-Sensing

 

Single-Scale Sensing

    TIP-CSNet     

    W. Shi et al., Image Compressed Sensing using Convolutional Neural Network, IEEE Trans. Image Process, 2019.

 

    Perceptual-CS

    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     

    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   

    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  

    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       

    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       

    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     

    S. Schneider, "A deep learning approach to compressive sensing with convolutional autoencoders," tech. report, 2016.

 

    ReconNet         

    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

    Scalable Compressed Sensing Network (SCSNet)    

    W. Shi et al., Scalable Convolutional Neural Network for Image Compressed Sensing, CVPR 2019.

 

    DoC-DCS     

    T. N. Canh and B. Jeon, "Difference of Convolution for Deep Compressive Sensing," IEEE International Conference on Imave Processing (ICIP), 2019.

 

    DCSNet     

    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      

    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     

    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

    DeepFlatCam  - Available soon

    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.)

    D

    CS-GAN- Available Soon from DeepMind

    Yan Wu, Mihaela Rosca, Timothy Lillicrap, Deep Compressive Sensing, Arxiv 2019.

 

    F-CSRG     

    Shaojie Xu, Sihan Zeng, Justin Romberg, "Fast Compressive Sensing Recovery Using Generative Models with Structured Latent Variables ," arXiv:1806.10175, 2019.

 

    L1AE     

    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     

    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      

    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

    H. Kerstin et al., "Learning a variational network for reconstruction of accelerated MRI data," Magnetic Resonance in Medicine, vol. 79, no. 6, 2018.

 

    CSMRI   

    M. Seitzer et al., "Adversarial and Perceptual Refinement Compressed Sensing MRI Reconstruction," MICCAI 2018.

 

    KCS-Net     

    T. N. Canh and B. Jeon, "Deep Learning-Based Kronecker Compressive Imaging", IEEE International Conference on Consumer Electronic Asia, 2018

 

    DAGAN      

    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         

    M. Illiasdis, L. Spinoulas, A. K. Katsaggelos, "Deep fully-connected networks for video compressive sensing," Elsevier Digital Signal Processing, vol. 72, 2018.

 

    CSVideoNet      

    K. Xu and F. Ren, "SVideoNet: A Recurrent Convolutional Neural Network for Compressive Sensing Video Reconstruction," arXiv:162.05203, 2018.

 

    SADN     

    Qiegen Liu and Henry Leung, Synthesis-analysis deconvolutional network for compressed sensing, IEEE International Conference on Image Processing, 2017.

 

    CSGM     

    A. Bora, A. Jalal, A. G. Dimakis, "Compressed sensing using Generative Models," arXiv:1703.03208, 2017.

 

    Learned D-AMP     

    C. A. Metzler et al., "Learned D-AMP: Principled Neural Network based Compressive Image Recovery," Advances in Neural Information Processing Systems, 2017.

 

    Deep-Ternary     

    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     

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

 

其他

    LIS-DL     

    Abdelrahman Taha, Muhammad Alrabeiah, Ahmed Alkhateeb, "Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning," arXiv:1904.10136, Apr 2019.

 

    VAE-GANs     

    Vineet Edupuganti, Morteza Mardani, Joseph Cheng, Shreyas Vasanawala, John Pauly, "VAE-GANs for Probabilistic Compressive Image Recovery: Uncertainty Analysis," arxiv1901.1128, 2019.

 

    Sparse-Gen   

    Manik Dhar, Aditya Grover, Stefano Ermon, "Modeling Sparse Deviations for Compressed Sensing using Generative Models," International Conference on Machine Learning (ICML), 2018

 

    Super-LiDAR     

    Nathaniel Chodosh, Chaoyang Wang, Simon Lucey, "Deep Convolutional Compressed Sensing for LiDAR Depth Completion," arXiv:1803.08949, 2018.

 

    Unpaired-GANCS    

    Reconstruct under sampled MRI image

 

    CSGAN     

    M. Kabkab, P. Samangouei, and R. Chellappa, "Task-Aware Compressed Sensing with Generative Adversarial Networks," AAAI Conference on Artificial Intelligence, 2018

 

    US-CS       

    D. Perdios, A. Besson, M. Arditi, and J. Thiran, "A Deep Learning Approach to Ultrasound Image Recovery", IEEE International Ultranosics Symposium, 2017.

 

    DeepIoT    

    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       

    H. Palangi, R. Ward, and L. Deng, "Distributed Compressive Sensing: A Deep Learning Approach," IEEE Transaction on Signal Processing, vol. 64, no. 17, 2016.

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