Nonlocal hierarchical dictionary learning using wavelets for image denoising

Ruomei Yan, Ling Shao, Yan Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

175 Citations (Scopus)


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.
Original languageEnglish
Article number6576863
Pages (from-to)4689-4698
Number of pages10
JournalIEEE Transactions on Image Processing
Issue number12
Publication statusPublished - 7 Oct 2013


  • Image denoising
  • multi-scale
  • nonlocal
  • sparse coding
  • wavelets

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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