Survey of single image super-resolution reconstruction

• Published in: IET image processing Vol. 14; no. 11; pp. 2273 - 2290
• Main Authors: Li, Kai, Yang, Shenghao, Dong, Runting, Wang, Xiaoying, Huang, Jianqiang
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 18.09.2020; Wiley
• Subjects: 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

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...