Open AccessColour compressed sensing imaging via sparse difference and fractal minimisation recovery
Author(s) -
Liu Jixin,
Li Xiaofei,
Han Guang,
Sun Ning,
Du Kun,
Sun Quansen
Publication year - 2015
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.2014.0346
Subject(s) - minimisation (clinical trials) , compressed sensing , fractal , computer science , artificial intelligence , pattern recognition (psychology) , computer vision , mathematics , statistics , mathematical analysis
In colour compressed sensing (CS) imaging, the current two bottlenecks for application are (1) high computation cost of sparse representation (SR) with over‐complete dictionary and (2) unsatisfactory imaging quality of CS recovery with l 1 ‐norm minimisation. Thus, this study proposes a novel colour CS imaging framework. In the framework, two improvements are achieved: (1) the authors present the sparse difference to reduce the computation cost of SR in RGB colour imaging; (2) the authors use fractal dimension instead of l 1 ‐norm as the object function to actualise high quality CS recovery. The feasibility of our colour CS imaging framework is proved by sseveral experiments.