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Pytorch code for paper "Deep Networks for Compressed Image Sensing" and "Image Compressed Sensing Using Convolutional Neural Network"

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WenxueCui/CSNet-Pytorch

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CSNet-Pytorch

Pytorch code for paper

  • "Deep Networks for Compressed Image Sensing" ICME2017

  • "Image Compressed Sensing Using Convolutional Neural Network" TIP2019

Requirements and Dependencies

  • Ubuntu 16.04 CUDA 10.0
  • Python3 (Testing in Python3.5)
  • Pytorch 1.1.0
  • Torchvision 0.2.2

Details of Implementations

In our code, two model version are included:

  • simple version of CSNet (Similar with paper ICME2017)
  • Enhanced version of CSNet (local skip connection + global skip connection + resudial learning)

How to Run

Training CSNet

  • Preparing the dataset for training

  • Editing the path of training data in file train.py.

  • For CSNet training in terms of subrate=0.1:

python train.py --sub_rate=0.1 --block_size=32

Testing CSNet

  • Preparing the dataset for testing

  • Editing the path of trained model in file test.py and test_new.py.

  • For CSNet testing in terms of subrate=0.1: (ps: For this testing code, there is a big gap compared with the result in the publised paper. And I am confused about it. If you know the reason, please let me know. Thanks very much!)

python test.py --sub_rate=0.1 --block_size=32

  • For CSNet testing (new testing code) in terms of subrate=0.1:

python test_new.py --cuda --sub_rate=0.1 --block_size=32

CSNet results

Subjective results

image

Objective results

image

Additional instructions

  • For training data, you can choose any natural image dataset.
  • The training data is very important, if you can not achieve ideal result, maybe you can focus on the augmentation of training data or the structure of the network.
  • If you like this repo, Star or Fork to support my work. Thank you.
  • If you have any problem for this code, please email: wxcui@hit.edu.cn

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Pytorch code for paper "Deep Networks for Compressed Image Sensing" and "Image Compressed Sensing Using Convolutional Neural Network"

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