Authors: Obradović, Radovan
Janev, Marko 
Antić, Borislav
Crnojević, Vladimir
Petrović, Nemanja
Title: Robust sparse image denoising
Journal: Proceedings - International Conference on Image Processing, ICIP
First page: 2569
Last page: 2572
Conference: 18th IEEE International Conference on Image Processing, ICIP 2011; Brussels; Belgium; 11 September 2011 through 14 September 2011
Issue Date: 1-Dec-2011
ISBN: 978-1-457-71303-3
ISSN: 1522-4880
DOI: 10.1109/ICIP.2011.6116188
In this paper, we propose a novel method for denoising images corrupted by the mixture of the additive white Gaussian noise and the heavy tailed noise. The proposed method is based on robust statistical approach, i.e. application of M-estimators in the combination with l 1 sparse regularization technique. Thus, we perform the denoising by numerical solving of the convex optimization problem. Additionally, the developed method performs denoising adaptively in order to preserve the image details, by adjusting regularization coefficients according to the local variance. The proposed de-noising scheme produces excellent results, both objectively (in terms of PSNR and MSSIM) and subjectively and outperforms state-of-the-art filters for denoising heavy tailed noise in images.
Keywords: estimation | gradient methods | Image denoising | pursuit algorithms
Publisher: IEEE

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