Pytorch code for paper
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"Deep Networks for Compressed Image Sensing" ICME2017
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"Image Compressed Sensing Using Convolutional Neural Network" TIP2019
- Ubuntu 16.04 CUDA 10.0
- Python3 （Testing in Python3.5）
- Pytorch 1.1.0
- Torchvision 0.2.2
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)
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Preparing the dataset for training
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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
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Preparing the dataset for testing
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Editing the path of trained model in file
test.pyandtest_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
- 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