Generalised non‐locally centralised image de‐noising using sparse dictionary. Issue 7 (1st July 2018)

• Record Type: Journal Article
• Authors: Ji, Jian; Ren, Fen; Ji, Hua‐Feng; Yao, Ya‐Feng; Hou, Guo‐Fei
• Is Part Of: IET image processing. Volume 12:Issue 7(2018)
• Page Start: 1072
• Page End: 1078
• Publication Date: 2018-07-01
• Subjects: image denoising -- image coding -- principal component analysis -- image restoration -- singular value decomposition; generalised nonlocally centralised image denoising -- sparse dictionary -- comprehensive sparse representation model -- sparse coding problem -- dictionary updating -- learning dictionary construction -- denoising effectiveness -- nonlocally centralised sparse representation denoising algorithm -- principal component analysis method -- single‐complete dictionary -- sparse coding -- image restoration -- generalised nonlocally centralised sparse representation algorithm -- train dictionaries -- sparse analysis k‐singular value decomposition dictionary -- coded sub‐block dictionary -- signal‐to‐noise ratio; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0783
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Recently, image de‐noising algorithm based on sparse representation has received an increasing amount of attention. Such algorithms proposed a comprehensive sparse representation model, by solving the sparse coding problem and choosing the proper method for dictionary updating to achieve better de‐noising results. Therefore, the construction of learning dictionary has become one of the key problems that limit the de‐noising effectiveness. The non‐locally centralised sparse representation de‐noising algorithm uses principal component analysis method to achieve dictionary updating. Nevertheless, the instability of a single complete dictionary in sparse coding leads to erratic result in the process of the original image restoration. In this study, the authors present a new method named generalised non‐locally centralised sparse representation algorithm. In the proposed method, the authors cluster the training patches extracted from a set of example images into subspaces, and then train dictionaries for subspaces by sparse analysis k‐singular value decomposition dictionary, which is utilised to construct coded sub‐block dictionary to avoid the instable results caused by a single dictionary. Experiments show that the improved method has better signal‐to‐noise ratio and de‐noising effect compared with other methods.