Open AccessSingle‐image super resolution using evolutionary sparse coding technique
Author(s) -
Ahmadi Kaveh,
Salari Ezzatollah
Publication year - 2017
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.2016.0273
Subject(s) - sparse approximation , neural coding , computer science , k svd , artificial intelligence , pattern recognition (psychology) , image (mathematics) , coding (social sciences) , feature detection (computer vision) , feature extraction , image processing , computer vision , mathematics , statistics
Sparse coding (SC) has recently become a widely used tool in signal and image processing. The sparse linear combination of elements from an appropriately chosen over‐complete dictionary can represent many signal patches. SC applications have been explored in many fields such as image super resolution (SR), image‐feature extraction, image reconstruction, and segmentation. In most of these applications, learning‐based SC has provided an excellent image quality. SC involves two steps: dictionary construction and searching the dictionary using quadratic programming. This study focuses on the searching step and a new adaptive variation of genetic algorithm is proposed to search and find the optimum closest match in the dictionary. Also, inspired by the proposed evolutionary SC (ESC), a single‐image SR algorithm is proposed. A sparse representation for each patch of the low‐resolution input image is obtained by ESC and it is used to generate the high‐resolution output image. Experimental results show that the proposed ESC‐based method would lead to a better SR image quality.