A new estimator for image denoising using a 2D dual-tree M-band wavelet decomposition

Caroline Chaux 1 Laurent Duval 2 Amel Benazza-Benyahia 3 Jean-Christophe Pesquet 1
1 LIGM - Laboratoire d'Informatique Gaspard-Monge
2 IFPEN - IFP Energies nouvelles
3 MASC - Département Mathématiques Appliquées, Signaux et Communications
Abstract : We propose a new estimator for image denoising using a 2D dualtree M-band wavelet transform. Our work extends existing blockbased wavelet thresholding methods by exploiting simultaneously coef cients in the two M-band wavelet trees. The contributions of this paper are two-fold. Firstly, we perform a statistical analysis of the noise in the considered redundant decomposition. Secondly, we propose an ef cient method to remove the noise. Our approach relies on an extension of Stein's formula which allows us to take into account the speci c correlations of the noise components. Simulation results are then presented to validate the proposed method.
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Submitted on : Tuesday, July 9, 2013 - 3:57:22 PM
Last modification on : Thursday, February 7, 2019 - 4:44:57 PM
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Caroline Chaux, Laurent Duval, Amel Benazza-Benyahia, Jean-Christophe Pesquet. A new estimator for image denoising using a 2D dual-tree M-band wavelet decomposition. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'06), May 2006, United States. pp.249-252. ⟨hal-00621899⟩

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