中文版 | English
Title

Super-resolution and inpainting with degraded and upgraded generative adversarial networks

Author
Corresponding AuthorZheng,Feng
Publication Years
2020
ISSN
1045-0823
Source Title
Volume
2021-January
Pages
645-651
Abstract
Image super-resolution (SR) and image inpainting are two topical problems in medical image processing. Existing methods for solving the problems are either tailored to recovering a high-resolution version of the low-resolution image or focus on filling missing values, thus inevitably giving rise to poor performance when the acquisitions suffer from multiple degradations. In this paper, we explore the possibility of super-resolving and inpainting images to handle multiple degradations and therefore improve their usability. We construct a unified and scalable framework to overcome the drawbacks of propagated errors caused by independent learning. We additionally provide improvements over previously proposed super-resolution approaches by modeling image degradation directly from data observations rather than bicubic downsampling. To this end, we propose HLH-GAN, which includes a high-to-low (H-L) GAN together with a low-to-high (L-H) GAN in a cyclic pipeline for solving the medical image degradation problem. Our comparative evaluation demonstrates that the effectiveness of the proposed method on different brain MRI datasets. In addition, our method outperforms many existing super-resolution and inpainting approaches.
SUSTech Authorship
Corresponding
Language
English
URL[Source Record]
Indexed By
Funding Project
Guangdong Provincial Key Laboratory of Urology[2017KSYS008];Guangdong Provincial Key Laboratory of Urology[2020B121201001];National Natural Science Foundation of China[61972188];
EI Accession Number
20205009609686
EI Keywords
Medical imaging ; Medical problems ; Image enhancement ; Magnetic resonance imaging ; Optical resolving power
ESI Classification Code
Biomedical Engineering:461.1 ; Magnetism: Basic Concepts and Phenomena:701.2 ; Artificial Intelligence:723.4 ; Light/Optics:741.1 ; Imaging Techniques:746
Scopus EID
2-s2.0-85097340315
Data Source
Scopus
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/406302
DepartmentSouthern University of Science and Technology
Affiliation
1.Malong Technologies,
2.Shenzhen Malong Artificial Intelligence Research Center,China
3.Depatment of Computer Science and Technology,Southern University of Science and Technology,
4.Research Institute of Trustworthy Autonomous Systems,
5.Purdue University,United States
6.Inception Institute of Artificial Intelligence,
Corresponding Author AffilicationSouthern University of Science and Technology
Recommended Citation
GB/T 7714
Huang,Yawen,Zheng,Feng,Wang,Danyang,et al. Super-resolution and inpainting with degraded and upgraded generative adversarial networks[C],2020:645-651.
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