Open AccessSurvey of single image super‐resolution reconstruction
Author(s) -
Li Kai,
Yang Shenghao,
Dong Runting,
Wang Xiaoying,
Huang Jianqiang
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.1438
Subject(s) - computer science , artificial intelligence , preprocessor , interpolation (computer graphics) , deep learning , image scaling , image (mathematics) , iterative reconstruction , computer vision , feature (linguistics) , image resolution , process (computing) , artificial neural network , pattern recognition (psychology) , pyramid (geometry) , image processing , mathematics , linguistics , philosophy , geometry , operating system
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.