Landweber Iteration-Inspired Network for Image Super-Resolution

Xiaohui Li; Ling Wang; Xinbo LiuNouman Ali

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Landweber Iteration-Inspired Network for Image Super-Resolution

机译：Landweber Iteration-Inspired Network for Image Super-Resolution

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Image super-resolution (SR) is one of the classical ill-posed image processing issues to generate high-resolution (HR) images from given low-resolution (LR) instances. Recent SR works aim to find an elaborate convolutional neural network (CNN) design and regard it as an end-to-end filter to map the image from LR space to HR space. However, seldom of them concentrate on the mathematical proof of network design or consider the problem from an optimization perspective. In this paper, we investigate the image SR based on the Landweber iteration method, which is an effective optimization method to find a feasible solution for the ill-posed problem. By considering the issue from the optimization perspective, we design a corresponding Landweber iteration-inspired network to adaptively learn the parameters and find the HR results. Experimental results show the proposed network achieves competitive or better subjective and objective performance than other state-of-the-art methods with fewer parameters and computational costs.

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《Mathematical Problems in Engineering: Theory, Methods and Applications》 |2022年第29期|1.1-1.10|共10页
作者
Xiaohui Li; Ling Wang; Xinbo LiuNouman Ali;
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School of Electronics and Information Engineering Liaoning University of Technology Jinzhou;

School of Materials Science and Engineering Yingkou Institute of Technology Yingkou;

Key Laboratory of Security for Network and Data in Industrial Internet of Liaoning Province Liaoning University of Technology Jinzhou ||SolBridge International School of Business Woosong University Daejeon;

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Landweber Iteration-Inspired Network for Image Super-Resolution

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