分段光滑图像变分分割的Split-Bregman迭代方法

he classical variational level set methods for image segmentation have two demerits of local minimization and low efficiency. The famous variational level set model-Chan-Vese mode for piecewise smooth image segmentation is transformed to a global convex segmentation one to avoid the initialization of level set functions on the results of segmentation, then, the Split-Bregman iteration method is used to implement the minimization process through simple and fast soft threshold formulas and Laplacian computation based on two alternative subproblems of minimization. Numerical examples validate the algorithm presented in this paper.