Experiments in Wavelet Shrinkage Denoising

Experiments in Wavelet Shrinkage Denoising

Previous simulation experiments for the comparison of wavelet shrinkage denoising methods have failed to demonstrate significant differences between methods. Such differences have never been clearly demonstrated due to the use of qualitative comparisons or of quantitative comparisons that suffered from insufficient sample size and/or absent confidence intervals for the figure of merit

Denoising through wavelet shrinkage: an empirical study

Adaptive Probabilistic Wavelet Shrinkage for Image Denoising

We study a Bayesian wavelet shrinkage approach for natural images based on a probability that a given coefficient contains a significant noise-free component, which we call “signal of interest”. First we develop new subband adaptive wavelet shrinkage method of this kind for the generalized Laplacian prior for noise free coefficients. We compare the new shrinkage approach with other subband adap...

Relations between Soft Wavelet Shrinkage and Total Variation Denoising

Soft wavelet shrinkage and total variation (TV) denoising are two frequently used techniques for denoising signals and images, while preserving their discontinuities. In this paper we show that – under specific circumstances – both methods are equivalent. First we prove that 1-D Haar wavelet shrinkage on a single scale is equivalent to a single step of TV diffusion or regularisation of two-pixe...

Image Denoising Using Tree-based Wavelet Subband Correlations and Shrinkage

In this paper we describe new methods of denoising images which combine wavelet shrinkage with properties related to the statistics of quad-trees of wavelet transform values for natural images. They are called Tree-Adapted Wavelet Shrinkage (TAWS) methods. The shift-averaged version of TAWS produces denoisings which are comparable to state of the art denoising methods, such as cycle-spin thresh...