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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Vydáno v:	IET image processing Ročník 14; číslo 11; s. 2273 - 2290
Hlavní autoři:	Li, Kai, Yang, Shenghao, Dong, Runting, Wang, Xiaoying, Huang, Jianqiang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 18.09.2020 Wiley
Témata:	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
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Popis
Shrnutí:	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 neural network and solve the problem of image super-resolution reconstruction by constructing a deep-level network for end-to-end training. The currently used deep learning models can divide the SISR model into four types: interpolation-based preprocessing-based model, original image processing based model, hierarchical feature-based model, and high-frequency detail-based model, or shared the network model. The current challenges for super-resolution reconstruction are mainly reflected in the actual application process, such as encountering an unknown scaling factor, losing paired LR–HR images, and so on.
ISSN:	1751-9659 1751-9667
DOI:	10.1049/iet-ipr.2019.1438