Deep Successive Convex Approximation for Image Super-Resolution

Xiaohui Li, Jinpeng Wang and Xinbo Liu ()
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Xiaohui Li: School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, China
Jinpeng Wang: School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, China
Xinbo Liu: SolBridge International School of Business, Woosong University, Daejeon 34613, Republic of Korea

Mathematics, 2023, vol. 11, issue 3, 1-13

Abstract: Image super-resolution (SR), as one of the classic image processing issues, has attracted increasing attention from researchers. As a highly ill-conditioned, non-convex optimization issue, it is difficult for image SR to restore a high-resolution (HR) image from a given low-resolution (LR) instance. Recent researchers have tended to regard image SR as a regression task and to design an end-to-end convolutional neural network (CNN) to predict the pixels directly, which lacks inherent theoretical analysis and limits the effectiveness of the restoration. In this paper, we analyze image SR from an optimization perspective and develop a deep successive convex approximation network (SCANet) for generating HR images. Specifically, we divide non-convex optimization into several convex LASSO sub-problems and use CNN to adaptively learn the parameters. To boost network representation, we use residual feature aggregation (RFA) blocks and devise a spatial and channel attention (SACA) mechanism to improve the restoration capacity. The experimental results show that the proposed SCANet can restore HR images more effectively than other works. Specifically, SCANet achieves higher PSNR/SSIM results and generates more satisfying textures.

Keywords: image super-resolution; successive convex approximation; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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