Super resolution reconstruction based on block matching and three‐dimensional filtering with sharpening. Issue 12 (1st December 2015)

• Record Type: Journal Article
• Authors: Kim, Yookyung; Oh, Han; Bilgin, Ali
• Is Part Of: IET image processing. Volume 9:Issue 12(2015)
• Page Start: 1048
• Page End: 1056
• Publication Date: 2015-12-01
• Subjects: image resolution -- image reconstruction -- image matching -- image filtering -- stereo image processing -- inverse problems -- compressed sensing -- estimation theory -- adaptive filters; super resolution reconstruction -- block matching -- 3D filtering -- inverse problem -- high resolution images -- low resolution images -- sparsity regularisation -- compressed sensing reconstruction -- natural image -- nonlocal estimation -- sharpening estimation -- adaptive filters; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2014.0566
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Super resolution (SR) reconstruction is often considered to be an inverse problem in the sense that unknown high resolution images are sought for giving low resolution images. Recent studies have shown that the sparsity regularisation used in compressed sensing (CS) reconstruction improves the performance of SR reconstruction. Furthermore, under the assumption that mutually similar regions exist within a natural image, non‐local (NL) estimation produces accurate estimates for given degraded images. The incorporation of this NL estimation in SR reconstruction has been shown to yield better reconstructions. In this study, the authors propose the use of block matching and three‐dimensional filtering with sharpening estimation as the regularisation constraint under the CS‐based SR framework. This estimation collects similar blocks and adaptively filters them by the shrinkage of the transform coefficients. It recovers detailed structures while attenuating ringing artefacts. In addition, a sharpening technique used in the estimation also emphasises edges. As a result, the proposed SR algorithm searches for the solution that is similar to this enhanced estimate from among all feasible solutions. The experimental results demonstrate that the proposed method provides high‐quality SR images, both numerically and subjectively.