A spectral graph wiener filter in graph fourier domain for improved image denoising

Abstract

A Wiener filtering scheme in graph Fourier domain is proposed for improving image denoising performance achieved by various spectral graph based denoising methods. The proposed Wiener filter is estimated by using graph Fourier coefficients of the noisy image after they are processed for denoising, to further improve the already achieved denoising accuracy as a post-processing step. It can be estimated from and applied to the entire image, or can be used patchwise in a locally adaptive manner. Our results indicate that the proposed step yields consistent accuracy improvement for different choices of weighted adjacency and graph Laplacian matrices used in computing the graph Fourier transform and for different processing methods used to denoise obtained transform coefficients. We obtain higher peak signal-to-noise ratio values than the BM3D method for some images