Noiseless Codelength in Wavelet Denoising

Noiseless Codelength in Wavelet Denoising

We propose an adaptive, data-driven thresholding method based on a recently developed idea of Minimum Noiseless Description Length (MNDL). MNDL Subspace Selection (MNDL-SS) is a novel method of selecting an optimal subspace among the competing subspaces of the transformed noisy data. Here we extend the application of MNDL-SS for thresholding purposes. The approach searches for the optimum thres...

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 using wavelet

In many domains, one needs image analysis to access several useful data. As an astrophysician looks at spectral image to recover space data, as a doctor identifies tissues with ultrasounds, scientists needs high quality images to work with. Unfortunately, in fields like medicine or astronomy, we often have perturbations on images taken from bodies or from space. Phenomena like reflections creat...

Robust wavelet denoising

For extracting a signal from noisy data, waveshrink and basis pursuit are powerful tools both from an empirical and asymptotic point of view. They are especially efficient at estimating spatially inhomogeneous signals when the noise is Gaussian. Their performance is altered when the noise has a long tail distribution, for instance, when outliers are present. We propose a robust wavelet-based es...

Denoising via Recursive Wavelet