Vol. 10, No. 2, 2017

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Total variation based denoising methods for speckle noise images

Arundhati Bagchi Misra, Ethan Lockhart and Hyeona Lim

Vol. 10 (2017), No. 2, 327–344
Abstract

In this paper, we introduce a new algorithm based on total variation for denoising speckle noise images. Total variation was introduced by Rudin, Osher, and Fatemi in 1992 for regularizing images. Chambolle proposed a faster algorithm based on the duality of convex functions for minimizing the total variation, but his algorithm was built for Gaussian noise removal. Unlike Gaussian noise, which is additive, speckle noise is multiplicative. We modify the original Chambolle algorithm for speckle noise images using the first noise equation for speckle denoising, proposed by Krissian, Kikinis, Westin and Vosburgh in 2005. We apply the Chambolle algorithm to the Krissian et al. speckle denoising model to develop a faster algorithm for speckle noise images.

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Keywords
anisotropic diffusion, speckle noise, denoising, total variation (TV) model, Chambolle algorithm, fast speckle denoising
Mathematical Subject Classification 2010
Primary: 68U10, 94A08
Secondary: 65M06, 65N06, 65K10, 49K20
Milestones
Received: 19 December 2015
Revised: 26 February 2016
Accepted: 19 March 2016
Published: 10 November 2016

Communicated by Kenneth S. Berenhaut
Authors
Arundhati Bagchi Misra
Department of Mathematical Sciences
Saginaw Valley State University
University Center, MI 48710
United States
Ethan Lockhart
Program in Applied Mathematics
The University of Arizona
Tuscon, AZ 85721
United States
Hyeona Lim
Department of Mathematics and Statistics
Mississippi State University
Mississippi State, MS 39762
United States