In this paper, a two-stage method for segmenting blurry images in the presence of Poisson or multiplicative Gamma noise is proposed. The method is inspired by a previous work on two-stage segmentation and the usage of an I-divergence term to handle the noise. The first stage of our method is to find a smooth solution to a convex variant of the Mumford--Shah model where the data-fidelity term is replaced by an I-divergence term. A primal-dual algorithm is adopted to efficiently solve the minimization problem. We prove the convergence of the algorithm and the uniqueness of the solution . Once is obtained, in the second stage, the segmentation is done by thresholding into different phases. The thresholds can be given by the users or can be obtained automatically by using any clustering method. In our method, we can obtain any -phase segmentation () by choosing thresholds after is found. Changing or the thresholds does not require to be recomputed. Experimental results show that our two-stage method performs better than many standard two-phase or multiphase segmentation methods for very general images, including antimass, tubular, magnetic resonance imaging, and low-light images.