Uniform and Textured Regions Separation in Natural Images Towards MPM Adaptive Denoising

摘要

Natural images consist of texture, structure and smooth re gions and this makes the task of filtering challenging mainly when it aims at edge and texture preservation. In this paper, we present a novel adap tive filtering technique based on a partition of the image to "noisy smooth zones" and "texture or edge + noise" zones. To this end, an analysis of local features is used to recover a statistical model that associates to each pixel a probability measure corresponding to a membership degree for each class. This probability function is then encoded in a new de noising process based on a MPM (Marginal Posterior Mode) estimation technique. The posterior density is computed through a non parametric density estimation method with variable kernel bandwidth that aims to adapt the denoising process to image structure. In our algorithm the selection of the bandwidth relies on a non linear function of the mem bership probabilities. Encouraging, experimental results demonstrate the potential of our approach.