This paper proposes a novel image segmentation framework that integrates alpha-stable mixture models ( \(\alpha \) SMM) with total variation (TV) regularization. Traditional image segmentation methods often rely on Gaussian Mixture Models (GMMs), which assume Gaussian noise characteristics. However, real-world images frequently exhibit impulsive noise or heavy-tailed distributions, rendering Gaussian assumptions inadequate. Our approach leverages the robustness of alpha-stable distributions, which can effectively model such non-Gaussian noise, and combines it with the geometric regularization power of total variation. We develop an Expectation-Maximization (EM) based algorithm to estimate the \(\alpha \) SMM parameters and incorporate the TV regularization within a unified variational framework. Experimental results on synthetic and real-world images demonstrate the promising performance and robustness of the proposed method, particularly in the presence of impulsive noise, compared to conventional GMM-based approaches.
