Image denoising based on sparse representation and gradient histogram. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Zhang, Mingli; Desrosiers, Christian
• Is Part Of: IET image processing. Volume 11:Issue 1(2017)
• Page Start: 54
• Page End: 63
• Publication Date: 2017-01-01
• Subjects: image denoising -- image texture -- image representation; image priors -- sparsity prior -- non‐local self‐similarity prior -- gradient histogram prior -- noise removal -- image texture -- texture enhancement -- false textures -- sparse representation -- sparse coding coefficients -- latent image -- image denoising -- natural texture appearance; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.0098
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Various image priors, such as sparsity prior, non‐local self‐similarity prior and gradient histogram prior, have been widely used for noise removal, while preserving the image texture. However, the gradient histogram prior used for texture enhancement sometimes generates false textures in the smooth areas. In order to address these problems, the authors propose a robust algorithm combining gradient histogram with sparse representation to obtain good estimates of the sparse coding coefficients of the latent image and realising image denoising while preserving the texture. The proposed model is solved by having a balance between over‐enhancement and over‐smoothing of the texture in order to preserve the natural texture appearance. Experimental results demonstrate the efficiency and effectiveness of the proposed method.