Image denoising using 2-D separable oversampled DFT modulated filter banks

Abstract

The direction-frequency selectivity of two-dimensional (2-D) separable oversampled discrete Fourier transform (DFT) modulated filter banks, showing that such 2-D filter banks can provide fine frequency tiling and efficient image representations with direction-frequency selectivity, is analysed. Moreover, the doubly local Wiener filtering is extended to the case using two 2-D separable oversampled DFT modulated filter banks, where the empirical subband energy distributions of the image are estimated by the two sets of directional windows matching the direction-frequency selectivity of the subband filters. The proposed algorithm is of low computational complexity and exhibits a great capability to preserve inhomogeneous textures, owing to the fact that 2-D separable DFT modulated bases are suited to represent oscillating patterns in images. The experimental results show that the proposed denoising algorithm is competitive with the existing algorithms with comparable computational complexity. Particularly, for images with abundant inhomogenous texture, it gives larger output peak signal-to-noise ratios (PSNRs) and achieves better visual effect in texture regions of images.