NL-CSNet-Pytorch

Pytorch code for paper "Image Compressed Sensing Using Non-local Neural Network" TMM 2021.

Note: this repo only shows the strategy of plugging the Non-local module (with non-local coupling loss constraint) into a simple CNN-based CS network (in the measurement domain and feature domain). For more details of the NL-CSNet atchitecture, please refer to the paper [here].

Framework

Requirements and Dependencies

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

How to Run

Training NL-CSNet

• Preparing the dataset for training

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

• For NL-CSNet training in terms of subrate=0.1:

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

Testing NL-CSNet

• Preparing the dataset for testing

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

• For NL-CSNet testing in terms of subrate=0.1:

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

NL-CSNet results

Subjective results

Objective results

Additional instructions

• For training data, you can choose any natural image dataset.
• If you like this repo, Star or Fork to support my work. Thank you.
• If you have any problem for this code, please email: [email protected]