Automatic estimation of the noise level function for adaptive blind denoising

Abstract : 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.
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Communication dans un congrès
24th European Signal Processing Conference (EUSIPCO), 2016, Aug 2016, Budapest, Hungary. Signal Processing Conference (EUSIPCO), 2016 24th European, pp.76 - 80, 2016, <10.1109/EUSIPCO.2016.7760213>
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Contributeur : Camille Sutour <>
Soumis le : mardi 31 janvier 2017 - 12:04:51
Dernière modification le : mardi 7 février 2017 - 01:05:07

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Camille Sutour, Jean-François Aujol, Charles-Alban Deledalle. Automatic estimation of the noise level function for adaptive blind denoising. 24th European Signal Processing Conference (EUSIPCO), 2016, Aug 2016, Budapest, Hungary. Signal Processing Conference (EUSIPCO), 2016 24th European, pp.76 - 80, 2016, <10.1109/EUSIPCO.2016.7760213>. <hal-01450723>

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