Automatic estimation of the noise level function for adaptive blind denoising
SUTOUR, Camille
Institut de Mathématiques de Bordeaux [IMB]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Institut für Numerische und Angewandte Mathematik
Institut de Mathématiques de Bordeaux [IMB]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Institut für Numerische und Angewandte Mathematik
SUTOUR, Camille
Institut de Mathématiques de Bordeaux [IMB]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Institut für Numerische und Angewandte Mathematik
< Reduce
Institut de Mathématiques de Bordeaux [IMB]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Institut für Numerische und Angewandte Mathematik
Language
en
Communication dans un congrès
This item was published in
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
English 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 ...Read more >
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.Read less <
Origin
Hal imported