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In this paper, we propose a novel locally statistical variational activecontour model based on I-divergence-TV denoising model, which hybrides geodesicactive contour (GAC) model with active contours without edges (ACWE) model, andcan be used to segment images corrupted by multiplicative gamma noise. Byadding a diffusion term into the level set evolution (LSE) equation of theproposed model, we construct a reaction-diffusion (RD) equation, which cangradually regularize the level set function (LSF) to be piecewise constant ineach segment domain and gain the stable solution. We further transform theproposed model into classic ROF model by adding a proximity term. Inspired by afast denoising algorithm proposed by Jia-Zhao recently, we propose two fastfixed point algorithms to solve SAR image segmentation question. Experimentalresults for real SAR images show that the proposed image segmentation model canefficiently stop the contours at weak or blurred edges, and can automaticallydetect the exterior and interior boundaries of images with multiplicative gammanoise. The proposed FPRD1/FPRD2 models are about 1/2 (or less than) of the timerequired for the SBRD model based on the Split Bregman technique.

A locally statistical active contour model for SAR image segmentation can be solved by denoising algorithms