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Communication dans un congrès

Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal

Abstract : Partial Differential equations (PDE), wavelets-based methods and neighborhood filters were proposed as locally adaptive machines for noise removal. Recently, Buades, Coll and Morel proposed the Non-Local (NL-) means filter for image denoising. This method replaces a noisy pixel by the weighted average of other image pixels with weights reflecting the similarity between local neighborhoods of the pixel being processed and the other pixels. The NL-means filter was proposed as an intuitive neighborhood filter but theoretical connections to diffusion and non-parametric estimation approaches are also given by the authors. In this paper we propose another bridge, and show that the NL-means filter also emerges from the Bayesian approach with new arguments. Based on this observation, we show how the performance of this filter can be significantly improved by introducing adaptive local dictionaries and a new statistical distance measure to compare patches. The new Bayesian NL-means filter is better parametrized and the amount of smoothing is directly determined by the noise variance (estimated from image data) given the patch size. Experimental results are given for real images with artificial Gaussian noise added, and for images with real image-dependent noise.
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Communication dans un congrès
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Déposant : Migration Prodinra <>
Soumis le : jeudi 4 juin 2020 - 03:15:07
Dernière modification le : vendredi 12 juin 2020 - 10:43:26

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Charles Kervrann, Jérôme Boulanger, Pierrick Coupé. Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal. First International Conference, SSVM - Scale Space and Variational Methods in Computer Vision - 2007, May 2007, Ischia, Italy. ⟨10.1007/978-3-540-72823-8_45⟩. ⟨hal-02758201⟩



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