Title: Enhanced image super-resolution using hierarchical generative adversarial network

Authors: Jianwei Zhao; Chenyun Fang; Zhenghua Zhou

Addresses: College of Sciences, China Jiliang University, Hangzhou, Zhejiang Province, 310018, China; Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province, China Jiliang University, Hangzhou, 310018, China ' College of Sciences, China Jiliang University, Hangzhou, Zhejiang Province, 310018, China ' College of Sciences, China Jiliang University, Hangzhou, Zhejiang Province, 310018, China

Abstract: Recently, generative adversarial networks (GAN) have been introduced in single-image super-resolution (SISR) to reconstruct more realistic high-resolution (HR) images. In this paper, we propose an effective SISR method, named super-resolution using hierarchical generative adversarial network (SRHGAN), based on the idea of GAN and the prior knowledge. Different from the existing GANs that focus on the depth of networks, our proposed method considers the prior knowledge in addition. That is, we introduce an edge extraction branch and an edge enhancement branch into GAN for considering the edge information. By means of the added edge loss in the loss function, the edge extraction branch and the edge enhancement branch will be trained to reconstruct the sharp edge well. Experimental results on several datasets illustrate that our reconstructed visual effect images are clearer and sharper than some related SISR methods.

Keywords: single image super-resolution; deep learning; generative adversarial network; hierarchical network; edge prior.

DOI: 10.1504/IJCSM.2022.124749

International Journal of Computing Science and Mathematics, 2022 Vol.15 No.3, pp.243 - 257

Received: 27 Jun 2021
Accepted: 30 Dec 2021
Published online: 08 Aug 2022 *

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