Image Segmentation Model Based on Local Image Fitting Energy and Split Bregman Method

PDE based image segmentation methods are the state-of-the-art methods due to the high accuracy and continuity of detected edges. Some examples are the region-scalable fitting (RSF) model and the local image fitting energy (LIF) model. But they suffer from the high computing complexity and instability. Recently, the globally convex method and the split Bregman method are introduced to overcome this problem. In this paper, a globally convex version of the LIF model is proposed and then the split Bregman method is used to solve the model. Experiments show that this model is more efficient than the RSF model and the LIF model while with similar segmentation results.