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|Abstract:||It was recently demonstrated in  that the denoising performance of Non-Local Means (NLM) can be improved at large noise levels by replacing the mean by the robust Euclidean median. Numerical experiments on synthetic and natural images showed that the latter consistently performed better than NLM beyond a certain noise level, and significantly so for images with sharp edges. The Euclidean mean and median can be put into a common regression (on the patch space) framework, in which the l(2) norm of the residuals is considered in the former, while the l(1) norm is considered in the latter. The natural question then is what happens if we consider l(p) (0 < p < 1) regression? We investigate this possibility in this paper.|
|Electronic Publication Date:||21-Oct-2013|
|Citation:||Chaudhury, Kunal N, Singer, Amit. (2013). Non-Local Patch Regression: Robust Image Denoising in Patch Space. 2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 1345 - 1349|
|Pages:||1345 - 1349|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)|
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