Skip to content
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing, CVPR2018 (PyTorch Code)
Python Shell
Branch: master
Clone or download

README.md

ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing [PyTorch version]

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

Jian Zhang, Bernard Ghanem , "ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing", CVPR 2018, [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.

[Old Tensorflow Version]

Introduction

With the aim of developing a fast yet accurate algorithm for compressive sensing (CS) reconstruction of natural images, we combine in this paper the merits of two existing categories of CS methods: the structure insights of traditional optimization-based methods and the speed of recent network-based ones. Specifically, we propose a novel structured deep network, dubbed ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a general L1 norm CS reconstruction model. To cast ISTA into deep network form, we develop an effective strategy to solve the proximal mapping associated with the sparsity-inducing regularizer using nonlinear transforms. All the parameters in ISTA-Net (\eg nonlinear transforms, shrinkage thresholds, step sizes, etc.) are learned end-to-end, rather than being hand-crafted. Moreover, considering that the residuals of natural images are more compressible, an enhanced version of ISTA-Net in the residual domain, dubbed ISTA-Net+, is derived to further improve CS reconstruction. Extensive CS experiments demonstrate that the proposed ISTA-Nets outperform existing state-of-the-art optimization-based and network-based CS methods by large margins, while maintaining fast computational speed.

ISTA-Net Figure 1. Illustration of the proposed ISTA-Net framework.

Contents

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

Test

Quick start

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

  2. Run the following scripts to test ISTA-Net models.

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

    # test scripts
    python TEST_CS_ISTA_Net.py --epoch_num 200 --cs_ratio 1 --layer_num 9
    python TEST_CS_ISTA_Net.py --epoch_num 200 --cs_ratio 4 --layer_num 9
    python TEST_CS_ISTA_Net.py --epoch_num 200 --cs_ratio 10 --layer_num 9
    python TEST_CS_ISTA_Net.py --epoch_num 200 --cs_ratio 25 --layer_num 9
    python TEST_CS_ISTA_Net.py --epoch_num 200 --cs_ratio 30 --layer_num 9
    python TEST_CS_ISTA_Net.py --epoch_num 200 --cs_ratio 40 --layer_num 9
    python TEST_CS_ISTA_Net.py --epoch_num 200 --cs_ratio 50 --layer_num 9
  3. Run the following scripts to test ISTA-Net+ models.

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

    # test scripts
    python TEST_CS_ISTA_Net_plus.py --epoch_num 200 --cs_ratio 1 --layer_num 9
    python TEST_CS_ISTA_Net_plus.py --epoch_num 200 --cs_ratio 4 --layer_num 9
    python TEST_CS_ISTA_Net_plus.py --epoch_num 200 --cs_ratio 10 --layer_num 9
    python TEST_CS_ISTA_Net_plus.py --epoch_num 200 --cs_ratio 25 --layer_num 9
    python TEST_CS_ISTA_Net_plus.py --epoch_num 200 --cs_ratio 30 --layer_num 9
    python TEST_CS_ISTA_Net_plus.py --epoch_num 200 --cs_ratio 40 --layer_num 9
    python TEST_CS_ISTA_Net_plus.py --epoch_num 200 --cs_ratio 50 --layer_num 9

The whole test pipeline

  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'.

Train

Prepare training data

  1. Trainding data (Training_Data.mat including 88912 image blocks) is in './data'. If not, please download it from GoogleDrive or BaiduPan [code: xy52].

  2. Place Training_Data.mat in './data' directory

Begin to train

  1. run the following scripts to train ISTA-Net models.

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

    # CS ratio 1, 4, 10, 25, 30, 40, 50
    # train scripts
    python Train_CS_ISTA_Net.py --cs_ratio 10 --layer_num 9
    python Train_CS_ISTA_Net.py --cs_ratio 25 --layer_num 9
    python Train_CS_ISTA_Net.py --cs_ratio 50 --layer_num 9
    python Train_CS_ISTA_Net.py --cs_ratio 1 --layer_num 9
    python Train_CS_ISTA_Net.py --cs_ratio 4 --layer_num 9
    python Train_CS_ISTA_Net.py --cs_ratio 30 --layer_num 9
    python Train_CS_ISTA_Net.py --cs_ratio 40 --layer_num 9

    We found that the re-trained ISTA-Net models may get a bit higher performance than the results reported in our paper.

  2. run the following scripts to train ISTA-Net+ models.

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

     # CS ratio 1, 4, 10, 25, 30, 40, 50
    # train scripts
    python Train_CS_ISTA_Net_plus.py --cs_ratio 10 --layer_num 9
    python Train_CS_ISTA_Net_plus.py --cs_ratio 25 --layer_num 9
    python Train_CS_ISTA_Net_plus.py --cs_ratio 50 --layer_num 9
    python Train_CS_ISTA_Net_plus.py --cs_ratio 1 --layer_num 9
    python Train_CS_ISTA_Net_plus.py --cs_ratio 4 --layer_num 9
    python Train_CS_ISTA_Net_plus.py --cs_ratio 30 --layer_num 9
    python Train_CS_ISTA_Net_plus.py --cs_ratio 40 --layer_num 9

Results

Quantitative Results

Visual Results

Citation

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

@inproceedings{zhang2018ista,
  title={ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing},
  author={Zhang, Jian and Ghanem, Bernard},
  booktitle={CVPR},
  pages={1828--1837},
  year={2018}
}

Acknowledgements

You can’t perform that action at this time.