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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Beschreibung
Zusammenfassung: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