Image super‐resolution reconstruction based on adaptive sparse representation. (27th September 2018)

• Record Type: Journal Article
• Authors: Xu, Mengxi; Yang, Yun; Sun, Quansen; Wu, Xiaobin
• Other Names: Laccetti Giuliano guestEditor.; Lapegna Marco guestEditor.; Montella Raffaele guestEditor.; Kosta Sokol guestEditor.; Wu Shaofei guestEditor.
• Is Part Of: Concurrency and computation. Volume 30:Number 24(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-09-27
• Subjects: dictionaries -- nonlocal self‐similarity -- sparse representation -- super‐resolution; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4968
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: There are two problems in global over‐complete dictionary: lack of adaptability to image local structure and low computational efficiency. Based on the study of the adaptive sparse representation reconstruction, this paper obtained a series of corresponding sub‐dictionaries by image block subset, then the optimal sub‐dictionary for each reconstruction image block is adaptively selected, which can be more accurately sparse represented modeled to improve the effect and efficiency of the algorithm. In order to promote the ability of sparse representation model, nonlocal self‐similarity prior item is introduced. Meanwhile, the nonlocal self‐similarity model is improved by using the idea of the bilateral filter, and the space distance restraint between pixels is introduced to better keep the image edge information. Moreover, the nonlocal self‐similar distance measure is improved to reduce the amount of calculation. Experimental results show that the proposed algorithm can effectively suppress noise effects and can maintain the image edge details, at the same time, there are certain advantages at both the peak signal to noise ratio (PSNR) and visual effects.