A fractional-order image segmentation model with application to low-contrast and piecewise smooth images

In this paper, we propose a two-stage image segmentation model based on structure tensor and fractional-order regularization. In the first stage, we use the fractional-order regularization to approximate the Hausdorff measure of the Mumford-Shah (MS) model. The existence and uniqueness of the solution is proved and the alternating direction implicit (ADI) scheme is used to find the solution of the modified MS model. In the second stage, a thresholding is used to induce the segmentation of the target. The superior performances of the proposed model are demonstrated by some comparative experimental results with several state-of-art methods.

Automated summary

This paper proposes a two-stage image segmentation model based on structure tensor and fractional-order regularization. In the first stage, fractional-order regularization is used to approximate the Hausdorff measure of the MS model. The solution is found using the ADI scheme. In the second stage, thresholding is used for target segmentation. The proposed model demonstrates superior performance compared to state-of-the-art methods.