Wavelet Shrinkage on Paths for Denoising of Scattered Data

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

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...

Denoising through wavelet shrinkage: an empirical study

Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency

Most simple nonlinear thresholding rules for wavelet-based denoising assume that the wavelet coefficients are independent. However, wavelet coefficients of natural images have significant dependencies. In this paper, we will only consider the dependencies between the coefficients and their parents in detail. For this purpose, new non-Gaussian bivariate distributions are proposed, and correspond...

Wavelet Shrinkage for Unequally Spaced Data Wavelet Shrinkage for Unequally Spaced Data

Wavelet shrinkage (WaveShrink) is a relatively new technique for nonparametric function estimation that has been shown to have asymptotic near-optimality properties over a wide class of functions. As originally formulated by Donoho and Johnstone, WaveShrink assumes equally spaced data. Because so many statistical applications (e.g., scatterplot smoothing) naturally involve unequally spaced data...