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The paper presents an image denoising scheme by combining a method that isbased on directional quasi-analytic wavelet packets (qWPs) with thestate-of-the-art Weighted Nuclear Norm Minimization (WNNM) denoising algorithm.The qWP-based denoising method (qWPdn) consists of multiscale qWP transform ofthe degraded image, application of adaptive localized soft thresholding to thetransform coefficients using the Bivariate Shrinkage methodology, andrestoration of the image from the thresholded coefficients from severaldecomposition levels. The combined method consists of several iterations ofqWPdn and WNNM algorithms in a way that at each iteration the output from onealgorithm boosts the input to the other. The proposed methodology couples theqWPdn capabilities to capture edges and fine texture patterns even in theseverely corrupted images with utilizing the non-local self-similarity in realimages that is inherent in the WNNM algorithm.  Multiple experiments, which compared the proposed methodology with sixadvanced denoising algorithms, including WNNM, confirmed that the combinedcross-boosting algorithm outperforms most of them in terms of both quantitativemeasure and visual perception quality.

Cross-boosting of WNNM Image Denoising method by Directional Wavelet Packets