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

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

Training data (Training_Data_Img91.mat) and other training models can be downloaded at GoogleDrive.

The code is tested on both Windows and Linux environments (Tensorflow: 1.2.0, CUDA8.0, cuDNN5.1) with Titan 1080Ti GPU.

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.

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

If you find our code helpful in your resarch or work, please cite our paper.

@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}
}