GAN-Based Image Super-Resolution with a Novel Quality Loss
Joint Authors
Zhang, Lijun
Zhang, Lin
Shen, Ying
Zhao, Shengjie
Zhu, Xining
Liu, Xiao
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-02-18
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhu, Xining& Zhang, Lin& Zhang, Lijun& Liu, Xiao& Shen, Ying& Zhao, Shengjie. 2020. GAN-Based Image Super-Resolution with a Novel Quality Loss. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1195761
Modern Language Association (MLA)
Zhu, Xining…[et al.]. GAN-Based Image Super-Resolution with a Novel Quality Loss. Mathematical Problems in Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1195761
American Medical Association (AMA)
Zhu, Xining& Zhang, Lin& Zhang, Lijun& Liu, Xiao& Shen, Ying& Zhao, Shengjie. GAN-Based Image Super-Resolution with a Novel Quality Loss. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1195761
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195761