ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing

ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing

Jian Zhang and Bernard  Ghanem,
"ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing",
Conference on Computer Vision and Pattern Recognition (CVPR 2018)
Jian Zhang, Bernard Ghanem
compressive sensing, optimization inspired deep networks
2018
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 (e.g. non-linear 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.