Improved Nonlocal Means Based on Pre-Classification and Invariant Block Matching

Ruomei Yan, Ling Shao, Sascha Cvetkovic, Jan Klijn

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)


One of the most popular image denoising methods based on self-similarity is called nonlocal means (NLM). Though it can achieve remarkable performance, this method has a few shortcomings, e.g., the computationally expensive calculation of the similarity measure, and the lack of reliable candidates for some nonrepetitive patches. In this paper, we propose to improve NLM by integrating Gaussian blur, clustering, and rotationally invariant block matching (RIBM) into the NLM framework. Experimental results show that the proposed technique can perform denoising better than the original NLM both quantitatively and visually, especially when the noise level is high.
Original languageEnglish
Pages (from-to)212-218
JournalJournal of Display Technology
Issue number4
Early online date23 Mar 2012
Publication statusPublished - Apr 2012


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