Involve: A Journal of Mathematics

  • Involve
  • Volume 10, Number 2 (2017), 327-344.

Total variation based denoising methods for speckle noise images

Arundhati Bagchi Misra, Ethan Lockhart, and Hyeona Lim

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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.

Article information

Source
Involve, Volume 10, Number 2 (2017), 327-344.

Dates
Received: 19 December 2015
Revised: 26 February 2016
Accepted: 19 March 2016
First available in Project Euclid: 13 December 2017

Permanent link to this document
https://projecteuclid.org/euclid.involve/1513135634

Digital Object Identifier
doi:10.2140/involve.2017.10.327

Mathematical Reviews number (MathSciNet)
MR3574304

Zentralblatt MATH identifier
1366.94046

Subjects
Primary: 68U10: Image processing 94A08: Image processing (compression, reconstruction, etc.) [See also 68U10]
Secondary: 65M06: Finite difference methods 65N06: Finite difference methods 65K10: Optimization and variational techniques [See also 49Mxx, 93B40] 49K20: Problems involving partial differential equations

Keywords
anisotropic diffusion speckle noise denoising total variation (TV) model Chambolle algorithm fast speckle denoising

Citation

Bagchi Misra, Arundhati; Lockhart, Ethan; Lim, Hyeona. Total variation based denoising methods for speckle noise images. Involve 10 (2017), no. 2, 327--344. doi:10.2140/involve.2017.10.327. https://projecteuclid.org/euclid.involve/1513135634


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