Image Denoising Using Asymmetric Gaussian Mixture Models

Abstract

Finite mixture models are widely applied in image denoising because they have sound mathematical basis and the results are interpretable. In a manner of speaking, they can give a mathematical description of natural images by clustering. Usually assume that per-component of natural images follow a mixture of Gaussian(GMM) when doing image denoising. However, it is well-know that most of natural images are intricate and of which the distribution is highly non-Gaussian. So there remain problems that GMM cannot fix. In this paper, we introduce the asymmetric Gaussian mixture models into the finite mixture model, in which GMM is a special case. Asymmetric Gaussian Mixture (AGM) can model asymmetric distribution which is more conform to the data of natural images. We do some experiments in image denoising under different noise scales and types. The AGM can have better results compare to The GMM.