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