Adaptive Nonlinear Image Denoising and Restoration Using a Cooperative Bayesian Estimation Approach

A novel nonlinear cooperative approach to image denoising and restoration is presented. Samples from the image field with similar characteristics are first grouped into clusters by first performing image decomposition based on the Mumford-Shah model using a total variational framework and performing fuzzy cmeans clustering within each image partition. Samples within each cluster are then aggregated using an cooperative Bayesian estimation method based on information from all the samples to provide a nonlinear estimate of the original image. The proposed method exploits information redundancy within each cluster to denoise and restore the original image. Furthermore, the proposed cooperative Bayesian estimation method is capable of suppressing noise and reducing image degradation while preserving image detail by utilizing intracluster statistics. The experimental results using different types of images demonstrate that the proposed algorithm provides state-of-the-art image denoising pe...

• Type: Conference
• Year: 2008
• Where: ICVGIP
• Authors: Akshaya Kumar Mishra, Alexander Wong, David A. Clausi, Paul W. Fieguth
• Keywords: Computer Vision, Cooperative Bayesian Estimation, ICVGIP 2008, Image, Image Denoising