Abstract
A variational model for image segmentation consists of a data term and a regularization term. Usually, the data term is chosen as squared \(\text{ L }_{2}\) norm, and the regularization term is determined by the prior assumption. In this paper, we present a novel model in the framework of MAP (maximum a posteriori). A new iteratively reweighted \(\text{ L }_{2}\) norm is used in the data term, which shares the advantages of \(\text{ L }_{2}\) and mixed \(\text{ L }_{21}\) norm. An edge weighting function is addressed in the regularization term, which enforces the ability to reduce the outlier effects and preserve edges. An improved region-based graph cuts algorithm is proposed to solve this model efficiently. Numerical experiments show our method can get better segmentation results, especially in terms of removing outliers and preserving edges.
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