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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
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Abstract |
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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. |
Keywords
anisotropic diffusion, speckle noise, denoising, total
variation (TV) model, Chambolle algorithm, fast speckle
denoising
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Mathematical Subject Classification 2010
Primary: 68U10, 94A08
Secondary: 65M06, 65N06, 65K10, 49K20
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Milestones
Received: 19 December 2015
Revised: 26 February 2016
Accepted: 19 March 2016
Published: 10 November 2016
Communicated by Kenneth S. Berenhaut
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Authors |
| Arundhati Bagchi Misra | |
| Department of Mathematical
Sciences Saginaw Valley State University University Center, MI 48710 United States |
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| Ethan Lockhart | |
| Program in Applied Mathematics The University of Arizona Tuscon, AZ 85721 United States |
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| Hyeona Lim | |
| Department of Mathematics and
Statistics Mississippi State University Mississippi State, MS 39762 United States |
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