  The paper presents an image denoising algorithm by combining a method that is
based on directional quasi-analytic wavelet packets (qWPs) with the popular
BM3D algorithm. The qWPs and its corresponding transforms are designed in [1].
The denoising algorithm qWP (qWPdn) applies an adaptive localized soft
thresholding to the transform coefficients using the Bivariate Shrinkage
methodology. The combined method consists of several iterations of qWPdn and
BM3D algorithms, where the output from one algorithm updates the input to the
other (cross-boosting).The qWPdn and BM3D methods complement each other. The
qWPdn capabilities to capture edges and fine texture patterns are coupled with
utilizing the sparsity in real images and self-similarity of patches in the
image that is inherent in the BM3D. The obtained results are quite competitive
with the best state-of-the-art algorithms. We compare the performance of the
combined methodology with the performances of cptTP-CTF6, DAS-2 algorithms,
which use directional frames, and the BM3D algorithm. In the overwhelming
majority of the experiments, the combined algorithm outperformed the above
methods.
