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A Review of Single Image Super-resolution Based on Deep Learning
N. Zhang,W. Y.-C,Z. X and X. D.-D
2020
发表期刊Zidonghua Xuebao/Acta Automatica Sinica
ISSN2544156
卷号46期号:12页码:2479-2499
摘要Super-resolution (SR) refers to an estimation of high resolution (HR) image from one or more low resolution (LR) observations of the same scene, usually employing digital image processing and machine learning techniques. This technique can effectively improve image resolution without upgrading hardware devices. In recent years, deep learning has developed rapidly in the image field, and it has brought promising prospects for single-image super-resolution (SISR). This paper summarizes the research status and development tendency of the current SISR methods based on deep learning. First, we introduce a series of networks characteristics for SISR, and analysis of these networks in the structure, input, loss function, scale factors and evaluation criterion are given. Then according to the experimental results, we discuss the existing problems and solutions. Finally, the future development and challenges of the SISR methods based on deep learning are presented. Copyright 2020 Acta Automatica Sinica. All rights reserved.
DOI10.16383/j.aas.c190031
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条目标识符http://ir.ciomp.ac.cn/handle/181722/64863
专题中国科学院长春光学精密机械与物理研究所
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N. Zhang,W. Y.-C,Z. X and X. D.-D. A Review of Single Image Super-resolution Based on Deep Learning[J]. Zidonghua Xuebao/Acta Automatica Sinica,2020,46(12):2479-2499.
APA N. Zhang,W. Y.-C,Z. X and X. D.-D.(2020).A Review of Single Image Super-resolution Based on Deep Learning.Zidonghua Xuebao/Acta Automatica Sinica,46(12),2479-2499.
MLA N. Zhang,W. Y.-C,Z. X and X. D.-D."A Review of Single Image Super-resolution Based on Deep Learning".Zidonghua Xuebao/Acta Automatica Sinica 46.12(2020):2479-2499.
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