NDLI: Robust sparse image denoising

Content Provider	IEEE Xplore Digital Library
Author	Obradovic, R. Janev, M. Antic, B. Crnojevica, V. Petrovic, N.I.
Copyright Year	2011
Description	Author affiliation: Faculty of Engineering, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia (Obradovic, R.; Janev, M.; Crnojevica, V.; Petrovic, N.I.) \|\| Interdisciplinary Center for Scientific Computing, University of Heidelberg, Germany (Antic, B.)
Abstract	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 ℓ1 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.
Starting Page	2569
Ending Page	2572
File Size	426091
Page Count	4
File Format	PDF
ISBN	9781457713040
ISSN	15224880
e-ISBN	9781457713033
e-ISBN	9781457713026
DOI	10.1109/ICIP.2011.6116188
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-11
Publisher Place	Belgium
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness PSNR Noise reduction Image denoising Additives Noise measurement gradient methods estimation pursuit algorithms
Content Type	Text
Resource Type	Article

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