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This is the test demo for "Deep networks for compressed image sensing." (ICME2017) Author: Wuzhen Shi Email: [email protected]

MATLAB 24.65% Makefile 1.18% TeX 10.67% Python 6.84% CSS 0.38% JavaScript 0.05% HTML 0.49% C++ 16.02% Cuda 36.05% C 1.41% Shell 2.23% M 0.03%

csnet's Introduction

CSNet

%-----------------------------------------------------------------------------------------------------

This is the test demo for "Deep networks for compressed image sensing." (ICME2017)

Author: Wuzhen Shi

Email: [email protected]

School of Computer Science of Technology, Harbin Institute of Technology, China

%-------------------------------------------------------------------------------------------------------

BibTex:

@inproceedings{Shi2017Deep,

title={Deep networks for compressed image sensing},

author={Shi, Wuzhen and Jiang, Feng and Zhang, Shengping and Zhao, Debin},

booktitle={IEEE International Conference on Multimedia and Expo},

pages={877-882},

year={2017},

}

Usage:

set different samplingRate (0.1 to 0.5) to obtain the same results as we report in the ICME2017 paper.

The release code has included the sampling subnetwork and the reconstruction subnetwork. It is very easy to separate them for different purposes.

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