Deep-Learned Regularization and Proximal Operator for Image Compressive Sensing

Chen, Zan, Guo, Wenlong, Feng, Yuanjing, Li, Yongqiang, Zhao, Changchen, Ren, Yi and Shao, Ling (2021) Deep-Learned Regularization and Proximal Operator for Image Compressive Sensing. IEEE Transactions on Image Processing. ISSN 1057-7149

[img]
Preview
PDF (Accepted_Manuscript) - Accepted Version
Download (6MB) | Preview

Abstract

Deep learning has recently been intensively studied in the context of image compressive sensing (CS) to discover and represent complicated image structures. These approaches, however, either suffer from nonflexibility for an arbitrary sampling ratio or lack an explicit deep-learned regularization term. This paper aims to solve the CS reconstruction problem by combining the deep-learned regularization term and proximal operator. We first introduce a regularization term using a carefully designed residual-regressive net, which can measure the distance between a corrupted image and a clean image set and accurately identify to which subspace the corrupted image belongs. We then address a proximal operator with a tailored dilated residual channel attention net, which enables the learned proximal operator to map the distorted image into the clean image set. We adopt an adaptive proximal selection strategy to embed the network into the loop of the CS image reconstruction algorithm. Moreover, a self-ensemble strategy is presented to improve CS recovery performance. We further utilize state evolution to analyze the effectiveness of the designed networks. Extensive experiments also demonstrate that our method can yield superior accurate reconstruction (PSNR gain over 1 dB) compared to other competing approaches while achieving the current state-of-the-art image CS reconstruction performance. The test code is available at https://github.com/zjut-gwl/CSDRCANet.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 01 Jul 2021 00:17
Last Modified: 16 Jul 2021 00:17
URI: https://ueaeprints.uea.ac.uk/id/eprint/80359
DOI: 10.1109/TIP.2021.3088611

Actions (login required)

View Item View Item