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

Jian Zhang, Bernard Ghanem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

821 Scopus citations


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 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. 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. Our source codes are available:
Original languageEnglish (US)
Title of host publication2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
PublisherIEEE Computer Society
Number of pages10
ISBN (Print)9781538664209
StatePublished - Dec 18 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


Dive into the research topics of 'ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing'. Together they form a unique fingerprint.

Cite this