ADMM-CSNet

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Pytorch codes for model in "ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing" (TPAMI 2019)

If you use these codes, please cite our paper:

[1] Yan Yang, Jian Sun, Huibin Li, Zongben Xu. ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing (TPAMI 2019).

http://gr.xjtu.edu.cn/web/jiansun/publications

All rights are reserved by the authors.

Xing Li and Yan Yang -2021/11/04. For more detail or traning data, feel free to contact: yangyan92@stu.xjtu.edu.cn

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Data link:

https://drive.google.com/drive/folders/1UhQ01pdmO11Agc5sM61Mt7KQTN9LytNt?usp=sharing

Download data and organise as follows in the directory:

### For dataset         
└── data
    ├── train
    ├── test
    ├── validate

Installation

This installation guide shows you how to set up the environment for running our code using conda.

First clone the ADMM-CSNet repository

git clone https://github.com/yangyan92/Pytorch_ADMM-CSNet.git
cd Pytorch_ADMM-CSNet

Then start a virtual environment with new environment variables nad

conda create --name ADMM-CSNet python=3.9
conda activate ADMM-CSNet 

Install PyTorch

pip install torch torchvision

Install all requirements

pip install -r requirements.txt

Usage:

1. The nonlinear layer of ADMM-CSNet in pytorch version was supported by the torchpwl package. Replace torchpwl package in your env with which we have provided in the torchpwl folder after installing.

2. We have provided a training mri image and mask in the data directory. Please replace the dataset downlowded in the google cloud. The full datasets contains 100 training data, 50 testing data and 50 validating data. The mask_20 directory in data represents 20% sample and so on.

3. The net of training was implemented by end-to-end in ADMM-CSNet of pytorch version. We have provided the final model of 20%, 30%, 40% sample in csnet_model directory, just replace the model name in test.py.

   Test:

   python test.py
   

4. For retraining you should change the data_dir and mask name in train.py and saved in the logs_csnet directory.

   Training:

   python train.py
   

5. Results

   Notice:

   The optimizer of pytorch ADMM-CSNet is adam as its avaliable and faster training in big data which of matlab version used by LBFGS may cause the difference in final results. The final tests below are testing to reconstruct complex-valued MR images with 1D Cartesian masks.

   sampling rate	psnr(dB)
   0.2	31.354
   0.3	34.365
   0.4	37.153

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