Active contours driven by local and global intensity fitting energy with
  application to SAR image segmentation and its fast solvers
Guangming Liu Qi Liu Jing Liang Quanying Sun
In this paper, we propose a novel variational active contour model based on
Aubert-Aujol (AA) 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. We transform the
proposed model into classic ROF model by adding a proximity term. Inspired by a
fast denosing 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 fast fixed point algorithms are robustness to
initialization contour, and can further reduce about 15% of the time needed for
algorithm proposed by Goldstein-Osher.