Nonlocal Hierarchical Dictionary Learning Using Wavelets for Image Denoising

Yan, Ruomei, Shao, Ling and Liu, Yan (2013) Nonlocal Hierarchical Dictionary Learning Using Wavelets for Image Denoising. IEEE Transactions on Image Processing, 22 (12). pp. 4689-4698. ISSN 1057-7149

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Official URL: http://dx.doi.org/10.1109/TIP.2013.2277813

Abstract

Exploiting the sparsity within representation models for images is critical for image denoising. The best currently available denoising methods take advantage of the sparsity from image self-similarity, pre-learned, and fixed representations. Most of these methods, however, still have difficulties in tackling high noise levels or noise models other than Gaussian. In this paper, the multiresolution structure and sparsity of wavelets are employed by nonlocal dictionary learning in each decomposition level of the wavelets. Experimental results show that our proposed method outperforms two state-of-the-art image denoising algorithms on higher noise levels. Furthermore, our approach is more adaptive to the less extensively researched uniform noise.

Item Type: Article
Uncontrolled Keywords: Image denoising, wavelets, sparse coding, multi-scale, nonlocal
Subjects: G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer Science and Digital Technologies
Depositing User: Paul Burns
Date Deposited: 10 Jun 2015 13:04
Last Modified: 03 Nov 2016 12:22
URI: http://nrl.northumbria.ac.uk/id/eprint/22823

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