GAN-Based Image Super-Resolution with a Novel Quality Loss. (18th February 2020)

• Record Type: Journal Article
• Authors: Zhu, Xining; Zhang, Lin; Zhang, Lijun; Liu, Xiao; Shen, Ying; Zhao, Shengjie
• Other Names: Perez-Cisneros Marco Guest Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2020(2020)
• Publication Date: 2020-02-18
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2020/5217429
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Single image super-resolution (SISR) has been a very attractive research topic in recent years. Breakthroughs in SISR have been achieved due to deep learning and generative adversarial networks (GANs). However, the generated image still suffers from undesired artifacts. In this paper, we propose a new method named GMGAN for SISR tasks. In this method, to generate images more in line with human vision system (HVS), we design a quality loss by integrating an image quality assessment (IQA) metric named gradient magnitude similarity deviation (GMSD). To our knowledge, it is the first time to truly integrate an IQA metric into SISR. Moreover, to overcome the instability of the original GAN, we use a variant of GANs named improved training of Wasserstein GANs (WGAN-GP). Besides GMGAN, we highlight the importance of training datasets. Experiments show that GMGAN with quality loss and WGAN-GP can generate visually appealing results and set a new state of the art. In addition, large quantity of high-quality training images with rich textures can benefit the results.