Enhanced Diffusion by Projected Curvelet for Segmentation without Guiding Information

In this paper, we present a new segmentation model, which makes uses of Curvelet´s advantages of edge preserving and noise averaging. The model first applies Lorentzian-function based diffusion for stable pixel clustering, and then projects boundaries by Curvelet transform (CT) to enhance edges and modify region smear in diffusion. In particular, we also propose a criterion to seek the appropriate moment for CT enhancement, it is fulfilled by comparing partition results of Lorentzian and Tukey-based functions. If the number of reduced regions between two adjacent segmentation rounds arrives a threshold, CT will be performed to prevent edge disappearing. Experiments show that this significant segmentation is resulted from CT´s properties of boundary keeping and denoising, the method is superior to many other PDE approaches.