Open AccessFast multi‐spectral image super‐resolution via sparse representation
Author(s) -
Mullah Helal Uddin,
Deka Bhabesh,
Prasad A.V.V.
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.2019.0714
Subject(s) - sparse approximation , artificial intelligence , computer science , pattern recognition (psychology) , image resolution , image (mathematics) , feature extraction , feature (linguistics) , representation (politics) , superresolution , resolution (logic) , computer vision , iterative reconstruction , k svd , inverse problem , mathematics , philosophy , linguistics , politics , political science , law , mathematical analysis
Sparse reconstruction is used to solve the inverse problem of single image super‐resolution (SR) as a patch‐based sparsity promoting regularisation problem. A coupled trained overcomplete dictionary from high‐resolution (HR) and low‐resolution (LR) image patches containing significant features is proposed using sparse representation to produce HR patches from their LR counterparts. In this study, the authors develop a multi‐core single image SR technique for LR multi‐spectral images based on patch‐wise sparse representation coupled with morphological component analysis driven feature extraction. Simulations are carried out to evaluate the proposed method using real remote sensing images of a few Indian satellites, RESOURCESAT‐2 and CARTOSAT‐2, as well as other satellites, such as QuickBird etc. Results are also compared with other existing SR methods to establish the superiority of the proposed method in terms of both objective metrics and visual analysis.