Multi-Scale-Average-Filter-Assisted Level Set Segmentation Model with Local Region Restoration Achievements

Abstract Segmentation of noisy images having light in the background it is a challenging task for the existing segmen-tation models. In this paper, we propose a new variational model for joint restoration and segmentation of noisy images having intensity inhomogeneity in the presence of high contrast light in the background. The proposed model combines statistical local region information of circular regions centered at each pixel with a multi-phase segmentation technique enabling inhomogeneous image restoration. The proposed model is written in the fuzzy set framework and solved by alternating direction minimization method of multipliers. Through experiments, we have tested our proposed model on different types of synthetic and real images in the presence of intensity in-homogeneity and demonstrate the accuracy and the robustness of the proposed model. In addition, the results are compared with state of the art two-phase and multi-phase methods and show that our method has superiority for images in the presence of noise and inhomogeneity.