Survey of single image super-resolution reconstruction

Image super-resolution reconstruction refers to a technique of recovering a high-resolution (HR) image (or multiple images) from a low-resolution (LR) degraded image (or multiple images). Due to the breakthrough progress in deep learning in other computer vision tasks, people try to introduce deep n...

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Veröffentlicht in:	IET image processing Jg. 14; H. 11; S. 2273 - 2290
Hauptverfasser:	Li, Kai, Yang, Shenghao, Dong, Runting, Wang, Xiaoying, Huang, Jianqiang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 18.09.2020 Wiley
Schlagworte:	computer vision deep neural network deep‐level network feature extraction GAN‐based deep learning method generative adversarial network hierarchical feature‐based model hyperspectral imaging image input information image processing image reconstruction image resolution interpolation learning (artificial intelligence) LR images LR–HR images medical imaging multiscale super‐resolution reconstruction network model neural nets or multiple images original image processing‐based model popular CNN‐based deep learning method remote sensing Review Article single image super‐resolution reconstruction super‐resolution images generative adversarial network LR images multiscale super-resolution reconstruction network model feature extraction learning (artificial intelligence) image resolution deep neural network deep-level network hierarchical feature-based model image processing original image processing-based model remote sensing image reconstruction popular CNN-based deep learning method interpolation single image super-resolution reconstruction LR–HR images computer vision super-resolution images GAN-based deep learning method or multiple images hyperspectral imaging neural nets medical imaging image input information
ISSN:	1751-9659, 1751-9667
Online-Zugang:	Volltext
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