This repository is for ISTA-Net⁺⁺ introduced in the following paper

Di You, Jingfen Xie (Equal Contribution), Jian Zhang "ISTA-Net⁺⁺: Flexible Deep Unfolding Network for Compressive Sensing", In 2021 IEEE International Conference on Multimedia and Expo (ICME), pages 1–6. IEEE, 2021. [pdf]

The code is built on PyTorch and tested on Ubuntu 16.04/18.04 and Windows 10 environment (Python3.x, PyTorch>=0.4) with 1080Ti GPU.

While deep neural networks have achieved impressive success in image compressive sensing (CS), most of them lack flexibility when dealing with multi-ratio tasks and multi-scene images in practical applications. To tackle these challenges, we propose a novel end-to-end flexible ISTA-unfolding deep network, dubbed ISTA-Net⁺⁺, with superior performance and strong flexibility. Specifically, by developing a dynamic unfolding strategy, our model enjoys the adaptability of handling CS problems with different ratios, i.e., multi-ratio tasks, through a single model. A cross-block strategy is further utilized to reduce blocking artifacts and enhance the CS recovery quality. Furthermore, we adopt a balanced dataset for training, which brings more robustness when reconstructing images of multiple scenes. Extensive experiments on four datasets show that ISTA-Net⁺⁺ achieves state-of-the-art results in terms of both quantitative metrics and visual quality. Considering its flexibility, effectiveness and practicability, our model is expected to serve as a suitable baseline in future CS research. Figure 1. Illustration of the proposed ISTA-Net⁺⁺ framework.

1. Test-CS
2. Train-CS
3. Results
4. Citation
5. Acknowledgements

1. All models for our paper have been put in './model'.

2. Please download sampling matrices from BaiduPan [code: rgd9].

3. Run the following scripts to test ISTA-Net⁺⁺ model.

   You can use scripts in file 'TEST_ISTA_Net_pp_scripts.sh' to produce results for our paper.

   # test scripts
   python TEST_ISTA_Net_pp.py  --cs_ratio 10 --layer_num 20
   python TEST_ISTA_Net_pp.py  --cs_ratio 20 --layer_num 20
   python TEST_ISTA_Net_pp.py  --cs_ratio 30 --layer_num 20
   python TEST_ISTA_Net_pp.py  --cs_ratio 40 --layer_num 20
   python TEST_ISTA_Net_pp.py  --cs_ratio 50 --layer_num 20

1. Prepare test data.

   The original test set11 is in './data'

2. Run the test scripts.

   See Quick start

3. Check the results in './result'.

Trainding data: Train400 Please download it from BaiduPan [code: 2o7t].

1. run the following scripts to train .

   You can use scripts in file 'Train_ISTA_Net_pp_scripts.sh' to train models for our paper.

   # train scripts
   python Train_ISTA_Net_pp.py --layer_num 20 --learning_rate 1e-4 --start_epoch 0 --end_epoch 400 --gpu_list 0

If you find the code helpful in your resarch or work, please cite the following papers.

@inproceedings{you2021ista,
  title={ISTA-Net++: Flexible Deep Unfolding Network for Compressive Sensing},
  author={You, Di and Xie, Jingfen and Zhang, Jian},
  booktitle={2021 IEEE International Conference on Multimedia and Expo (ICME)},
  pages={1--6},
  year={2021},
  organization={IEEE}
}