Abstract
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.
© 2012 IEEE
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