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

  • Pablo AriasEmail author
  • Mila Nikolova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10302)

Abstract

The non-local Bayesian (NLB) patch-based approach of Lebrun et al. [12] 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 ramifications 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.

Keywords

Negative Eigenvalue Image Patch Image Denoising Negative Weight Wiener Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.CMLA, ENS Cachan, Université Paris-SaclayCachanFrance
  2. 2.CMLA, ENS Cachan, CNRS, Université Paris-SaclayCachanFrance

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