Colour compressed sensing imaging via sparse difference and fractal minimisation recovery. Issue 5 (1st May 2015)

• Record Type: Journal Article
• Authors: Liu, Ji‐xin; Li, Xiao‐fei; Han, Guang; Sun, Ning; Du, Kun; Sun, Quan‐sen
• Is Part Of: IET image processing. Volume 9:Issue 5(2015)
• Page Start: 369
• Page End: 380
• Publication Date: 2015-05-01
• Subjects: image colour analysis -- compressed sensing -- fractals -- minimisation -- image representation -- sparse matrices; colour compressed sensing imaging -- sparse difference -- fractal minimisation recovery -- sparse representation -- overcomplete dictionary -- unsatisfactory imaging quality -- CS recovery -- l1‐norm minimisation -- computation cost -- RGB colour imaging -- fractal dimension -- SR computation cost; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2014.0346
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.