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ToolsYan, 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
Full text not available from this repository.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 |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| Depositing User: | Pure Connector |
| Date Deposited: | 07 Feb 2017 02:39 |
| Last Modified: | 31 Oct 2022 15:31 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/62321 |
| DOI: | 10.1109/TIP.2013.2277813 |
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