25 research outputs found
Compactly Supported Tensor Product Complex Tight Framelets with Directionality
Although tensor product real-valued wavelets have been successfully applied
to many high-dimensional problems, they can only capture well edge
singularities along the coordinate axis directions. As an alternative and
improvement of tensor product real-valued wavelets and dual tree complex
wavelet transform, recently tensor product complex tight framelets with
increasing directionality have been introduced in [8] and applied to image
denoising in [13]. Despite several desirable properties, the directional tensor
product complex tight framelets constructed in [8,13] are bandlimited and do
not have compact support in the space/time domain. Since compactly supported
wavelets and framelets are of great interest and importance in both theory and
application, it remains as an unsolved problem whether there exist compactly
supported tensor product complex tight framelets with directionality. In this
paper, we shall satisfactorily answer this question by proving a theoretical
result on directionality of tight framelets and by introducing an algorithm to
construct compactly supported complex tight framelets with directionality. Our
examples show that compactly supported complex tight framelets with
directionality can be easily derived from any given eligible low-pass filters
and refinable functions. Several examples of compactly supported tensor product
complex tight framelets with directionality have been presented
Coupling BM3D with directional wavelet packets for image denoising
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.Comment: 26 pages. arXiv admin note: substantial text overlap with
arXiv:2001.04899, arXiv:1907.01479; text overlap with arXiv:2008.0536