SUTOUR, Camille; AUJOL, Jean-François; DELEDALLE, Charles-Alban

doi:10.1109/EUSIPCO.2016.7760213

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SUTOUR, Camille
Institut de Mathématiques de Bordeaux [IMB]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Institut für Numerische und Angewandte Mathematik

AUJOL, Jean-François
Institut de Mathématiques de Bordeaux [IMB]

DELEDALLE, Charles-Alban
Institut de Mathématiques de Bordeaux [IMB]

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Signal Processing Conference (EUSIPCO), 2016 24th European, Signal Processing Conference (EUSIPCO), 2016 24th European, 24th European Signal Processing Conference (EUSIPCO), 2016, 2016-08-29, Budapest. 2016p. 76 - 80

Résumé en anglais

Image denoising is a fundamental problem in image processing and many powerful algorithms have been developed. However, they often rely on the knowledge of the noise distribution and its parameters. We propose a fully blind denoising method that first estimates the noise level function then uses this estimation for automatic denoising. First we perform the non-parametric detection of homogeneous image regions in order to compute a scatterplot of the noise statistics, then we estimate the noise level function with the least absolute deviation estimator. The noise level function parameters are then directly re-injected into an adaptive denoising algorithm based on the non-local means with no prior model fitting. Results show the performance of the noise estimation and denoising methods, and we provide a robust blind denoising tool.< Réduire

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Automatic estimation of the noise level function for adaptive blind denoising

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Résumé en anglais

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