Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32687
Title: Nonlocal hierarchical dictionary learning using wavelets for image denoising
Authors: Yan, R
Shao, L
Liu, Y 
Keywords: Image denoising
Multi-scale
Nonlocal
Sparse coding
Wavelets
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2013, v. 22, no. 12, 6576863, p. 4689-4698 How to cite?
Journal: IEEE transactions on image processing 
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.
URI: http://hdl.handle.net/10397/32687
ISSN: 1057-7149
EISSN: 1941-0042
DOI: 10.1109/TIP.2013.2277813
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