University of Cambridge > > Cambridge Image Analysis Seminars > Below the Surface of the Non-Local Bayesian Image Denoising Method

Below the Surface of the Non-Local Bayesian Image Denoising Method

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If you have a question about this talk, please contact Carola-Bibiane Schoenlieb.

The non-local Bayesian (NLB) patch-based approach of Lebrun, Buades, and Morel [1] is considered as a state-of-the-art method for the restoration of (color) images corrupted by white Gaussian noise. It gave rise to numerous rami fications like e.g., possible improvements, processing of various data sets and video. This article is the first attempt to analyse the method in depth in order to understand the main phenomena underlying its effectiveness. Our analysis, corroborated by numerical tests, shows several unexpected facts. In a variational setting, the first-step Bayesian approach to learn the prior for patches is equivalent to a pseudo-Tikhonov regularisation where the regularisation parameters can be positive or negative. Practically very good results in this step are mainly due to the aggregation stage – whose importance needs to be re-evaluated.

This is joint work with Pablo Arias.

Reference [1] Lebrun, M., Buades, A., Morel, J.M.: A nonlocal Bayesian image denoising algorithm. SIAM J . Imaging Sci.6(3), 1665-1688 (2013)

This talk is part of the Cambridge Image Analysis Seminars series.

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