An Improved Chan-Vese Model Integrated with L¹ Fitting Term

An improved CV model integrated with L¹ fitting term is proposed in this paper to enhance the robustness of the model for salt and pepper noise. First, a new fitting term is defined as a combination of L¹ fitting and L² fitting. Then, by appropriately choosing the weights of fitting, our proposed model allows flexible segmentation under various noise conditions. Finally, an alternating iterative algorithm is employed to solve the model numerically. Experiments on some synthetic and real images contaminated by different kinds of noise demonstrate that the proposed model is effective and robust for noise image segmentation. Moreover, compared with CV model, LBF model and VFCMS model, our model can achieve superior segmentation results for image corrupted by salt and pepper noise.