Spatially adapted total variational model for synthetic aperture radar image despeckling

Huiyan Liu; Jiying Liu; Fengxia Yan; Jobo Zhu; Faming Fang

doi:10.1117/1.JEI.22.3.033019

28 August 2013 Spatially adapted total variational model for synthetic aperture radar image despeckling

Huiyan Liu, Jiying Liu, Fengxia Yan, Jobo Zhu, Faming Fang

Author Affiliations +

Journal of Electronic Imaging, Vol. 22, Issue 3, 033019 (August 2013). https://doi.org/10.1117/1.JEI.22.3.033019

Abstract

An adaptive total variation method to reduce speckles with preservation of targets in synthetic aperture radar (SAR) images is investigated. Based on the gamma distribution of speckle, an adaptive total variational model is proposed with its fidelity term derived from a framework of weighted maximum likelihood estimation and its regularity term with constraints on the gradient of an image. It has merits of preserving textures and targets since the a priori distribution of noise is incorporated into the model and the weights are essentially image data driven, which can adaptively adjust the weights. The mathematical analysis is carried out, and proof of existence and uniqueness of a solution for the corresponding function is also presented. Theoretical analysis and experiments on both the simulated and real SAR images demonstrate that the method proposed here performs favorably.

Citation Download Citation

Huiyan Liu, Jiying Liu, Fengxia Yan, Jobo Zhu, and Faming Fang "Spatially adapted total variational model for synthetic aperture radar image despeckling," Journal of Electronic Imaging 22(3), 033019 (28 August 2013). https://doi.org/10.1117/1.JEI.22.3.033019

Published: 28 August 2013

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