In this paper, we propose a novel variational active contour model based on I-divergence-TV model to segment Synthetic aperture radar (SAR) images with multiplicative gamma noise, which hybrides edge-based model with region-based model. The proposed model can efficiently stop the contours at weak or blurred edges, and can automatically detect the exterior and interior boundaries of images. We incorporate the global convex segmentation method and split Bregman technique into the proposed model, and propose a fast fixed point algorithm to solve the global convex segmentation question[25]. Experimental results for synthetic images and real SAR images show that the proposed fast fixed point algorithm is robust and efficient compared with the state-of-the-art approach.

该论文提出了一种基于I-divergence-TV模型的新型变分主动轮廓模型，用于分割具有乘性伽马噪声的合成孔径雷达（SAR）图像，将基于边缘的模型与基于区域的模型相结合。所提出的模型能够在弱或模糊边缘处有效停止轮廓，并能自动检测图像的外部和内部边界。将全局凸分割方法和分裂布雷格曼技术融入所提出的模型，并提出了一种快速固定点算法来解决全局凸分割问题[25]。合成图像和实际SAR图像的实验结果表明，与现有技术相比，所提出的快速固定点算法具有鲁棒性和高效性。

基于I-散度-TV模型的SAR图像分割算法