Abstract

Some recent denoising methods are based on a statistical modeling of the image patches. In the literature, Gaussian models or Gaussian mixture models are the most widely used priors. In this presentation, after introducing the statistical framework of patch-based image denoising, I will propose some clues to answer the following questions: Why are these Gaussian priors so widely used? What information do they encode? In the second part, I will present a mixture model for noisy patches adapted to the high dimension of the patch space. This results in a denoising algorithm only based on statistical tools, which achieves state-of-the-art performance. Finally, I will discuss the limitations and some developments of the proposed method.