Abstract
In recent years, a great deal of effort has been expended on developing robust solutions for images quality degradation caused mainly by noise. In this paper, we explore this area of research and we propose a new unsupervised algorithm for both image and video denoising. Our solution is based on a flexible statistical mixture model driven by a finite mixtures of bounded generalized Gaussian distributions (BGGMD) which offers more flexibility in data modeling than the well known classical gaussian distributions which fail to fit the shape of heavy-tailed data produced by the presence of noise or outliers. The proposed framework takes into account also spatial information between neighboring pixels to be more robust and flexible, and able to provide smooth and accurate denoising results. For model's parameters estimation, we investigate the unsupervised expectation-maximization (EM) algorithm. In order to evaluate the performance of the proposed model, we conducted a series of extensive experiments. Obtained results are more encouraging than those obtained using similar approaches. These results show the robustness and flexibility of the proposed method to adapt different shapes of observed data and bounded support data in the case of noisy images and videos.