A locally statistical active contour model for SAR image segmentation
  can be solved by denoising algorithms
Guangming Liu Quanying Sun Jing Liang Qi Liu
In this paper, we propose a novel locally statistical variational active
contour model based on I-divergence-TV denoising model, which hybrides geodesic
active contour (GAC) model with active contours without edges (ACWE) model, and
can be used to segment images corrupted by multiplicative gamma noise. By
adding a diffusion term into the level set evolution (LSE) equation of the
proposed model, we construct a reaction-diffusion (RD) equation, which can
gradually regularize the level set function (LSF) to be piecewise constant in
each segment domain and gain the stable solution. We further transform the
proposed model into classic ROF model by adding a proximity term. Inspired by a
fast denoising algorithm proposed by Jia-Zhao recently, we propose two fast
fixed point algorithms to solve SAR image segmentation question. Experimental
results for real SAR images show that the proposed image segmentation model can
efficiently stop the contours at weak or blurred edges, and can automatically
detect the exterior and interior boundaries of images with multiplicative gamma
noise. The proposed FPRD1/FPRD2 models are about 1/2 (or less than) of the time
required for the SBRD model based on the Split Bregman technique.