Spatially Adaptive Image Denoising Based on Complex Wavelet Coefficients

We propose a computationally feasible adaptive noise smoothing algorithm that considers the local dependency characteristics of images under the complex dual-tree discrete wavelet transform (DWT). The wavelet coefficients with complex dual-tree discrete wavelet are assumed to be zero-mean Gaussian random variables. The variances of local statistics of each coefficient are estimated with a centered square-shaped window for every pixel. Some comparisons with the best available results will be given in order to illustrate the effectiveness of the proposed algorithm