Fast Poissonian image segmentation with a spatially adaptive kernel

The variational models with the goal of minimizing the local variation are widely used for the segmentation of the intensity inhomogeneous images recently. Local variation is a good measure for the images corrupted by Gaussian noise. However, in many applications such as astronomical imaging, electronic microscopy and positron emission tomography, Poisson noise often occurs in the observed images. To deal with this kind of images, we develop a novel segmentation model based on minimizing local generalized Kullback–Leibler (KL) divergence with a spatially adaptive kernel. A fast algorithm based on the split-Bregman method is proposed to solve the corresponding optimization problem. Numerical experiments for synthetic and real images demonstrate that the proposed model outperforms most of the current state-of-the-art methods in the present of Poisson noise.