Adaptive nonlinear image denoising and restoration using a cooperative Bayesian estimation approach

TitleAdaptive nonlinear image denoising and restoration using a cooperative Bayesian estimation approach
Publication TypeConference Paper
Year of Publication2008
AuthorsMishra, A., A. Wong, D. A. Clausi, and P. Fieguth
Conference Name6th Indian Conference on Computer Vision, Graphics Image Processing
Date Published12/2008
Conference LocationBhubaneswar, India
Keywordsadaptive nonlinear image denoising, Bayes methods, cooperative Bayesian estimation, fuzzy c-means clustering, fuzzy set theory, image decomposition, Image Denoising, image restoration, intracluster statistics, Mumford-Shah model, nonlinear cooperative approach, pattern clustering, peak signal-to-noise ratio, subjective visual quality

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 c-means 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 intra-cluster statistics. The experimental results using different types of images demonstrate that the proposed algorithm provides state-of-the-art image denoising performance in terms of both peak signal-to-noise ratio (PSNR) and subjective visual quality.