Chapter 6 - Image denoising, local factor analysis, Bayesian Ying-Yang harmony learning

Abstract

A new nonlocal-filtering method LFA-BYY is proposed for image demonizing via learning a local factor analysis (LFA) model from a polluted image under processing and then denoising the image by the learned LFA model. With the help of the Bayesian Ying-Yang (BYY) harmony learning, LFA-BYY can appropriately control the dictionary complexity and learn the noise intensity from the present image under processing, while the existing state-of-the-art methods either use a pretrained dictionary or a general basis, and require an accurate noise intensity estimation provided in advance. In comparison with BM3D, K-SVD, EPLL, and Msi on the benchmark Kodak dataset and additional medical data, experiments have shown that LFA-BYY has not only obtained competitive results on images polluted by a small noise but also outperformed these competing methods when the noise intensity increases beyond a point, especially with significant improvements as the noise intensity becomes large.