Image Denoising using Multi-Resolution Coefficient Support Based Empirical Wiener Filtering

In this paper, a new image denoising algorithm using simple thresholding operations and wavelet coefficient magnitude based Wiener filtering is proposed. A hard thresholding operation is initially performed on the noisy wavelet coefficients. The initial significance map is then refined by use of multi-resolution coefficient support map which considers the local spatial features of the image. As a final denoising step, optimal Wiener filtering is performed on the thresholded wavelet coefficients using only magnitude information. The performance of proposed algorithm is evaluated on standard test images and found to perform competitively to the state-of-art image denoising algorithms in the literature.