Learning a Generic Adaptive Wavelet Shrinkage Function for Denoising

by   Tobias Alt, et al.

The rise of machine learning in image processing has created a gap between trainable data-driven and classical model-driven approaches: While learning-based models often show superior performance, classical ones are often more transparent. To reduce this gap, we introduce a generic wavelet shrinkage function for denoising which is adaptive to both the wavelet scales as well as the noise standard deviation. It is inferred from trained results of a tightly parametrised function which is inherited from nonlinear diffusion. Our proposed shrinkage function is smooth and compact while only using two parameters. In contrast to many existing shrinkage functions, it is able to enhance image structures by amplifying wavelet coefficients. Experiments show that it outperforms classical shrinkage functions by a significant margin.



page 4


Neural shrinkage for wavelet-based SAR despeckling

The wavelet shrinkage denoising approach is able to maintain local regul...

Image denoising through bivariate shrinkage function in framelet domain

Denoising of coefficients in a sparse domain (e.g. wavelet) has been res...

Learning Integrodifferential Models for Image Denoising

We introduce an integrodifferential extension of the edge-enhancing anis...

Gamma-Minimax Wavelet Shrinkage with Three-Point Priors

In this paper we propose a method for wavelet denoising of signals conta...

Kurtosis control in wavelet shrinkage with generalized secant hyperbolic prior

The present paper proposes a bayesian approach for wavelet shrinkage wit...

Asymmetric prior in wavelet shrinkage

In bayesian wavelet shrinkage, the already proposed priors to wavelet co...

Wavelet Shrinkage and Thresholding based Robust Classification for Brain Computer Interface

A macaque monkey is trained to perform two different kinds of tasks, mem...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.