Fractal functions and wavelet expansions based on several scaling functions
Journal of Approximation Theory
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Short wavelets and matrix dilation equations
IEEE Transactions on Signal Processing
Balanced multiwavelet bases based on symmetric FIR filters
IEEE Transactions on Signal Processing
Some properties of symmetric-antisymmetric orthonormalmultiwavelets
IEEE Transactions on Signal Processing
A new prefilter design for discrete multiwavelet transforms
IEEE Transactions on Signal Processing
The discrete multiple wavelet transform and thresholding methods
IEEE Transactions on Signal Processing
Design of prefilters for discrete multiwavelet transforms
IEEE Transactions on Signal Processing
A general approach for analysis and application of discretemultiwavelet transforms
IEEE Transactions on Signal Processing
Interpolating multiwavelet bases and the sampling theorem
IEEE Transactions on Signal Processing
Translation-invariant denoising using multiwavelets
IEEE Transactions on Signal Processing
Multiwavelet bases with extra approximation properties
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
The application of multiwavelet filterbanks to image processing
IEEE Transactions on Image Processing
New image compression techniques using multiwavelets and multiwavelet packets
IEEE Transactions on Image Processing
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Multiwavelets, wavelets with several scaling functions, offer simultaneous orthogonality, symmetry, and short support, which is not possible with ordinary (scalar) wavelets. These properties make multiwavelets promising for signal processing applications, such as image denoising. The common approach for image denoising is to get the multiwavelet decomposition of a noisy image and apply a common threshold to each coefficient separately. This approach does not generally give sufficient performance. In this paper, we propose a multivariate thresholding technique for image denoising with multiwavelets. The proposed technique is based on the idea of restoring the spatial dependence of the pixels of the noisy image that has undergone a multiwavelet decomposition. Coefficients with high correlation are regarded as elements of a vector and are subject to a common thresholding operation. Simulations with several multiwavelets illustrate that the proposed technique results in a better performance.