Shrinkage is a well known and appealing denoising technique, introduced originally by
Donoho and Johnstone in 1994. The use of shrinkage for denoising is known to be optimal …

In various information processing tasks obtaining regularized versions of a noisy or
corrupted image data is often a prerequisite for successful use of classical image analysis …

The filling-in effect of diffusion processes has been successfully used in many image
analysis applications. Examples include image reconstructions in inpainting-based …

Convolutional neural networks (CNNs) often perform well, but their stability is poorly
understood. To address this problem, we consider the simple prototypical problem of signal …

This paper studies the connections between discrete two-dimensional schemes for shift-
invariant Haar wavelet shrinkage on one hand, and nonlinear diffusion on the other. We …

Local M-smoothers are interesting and important signal and image processing techniques
with many connections to other methods. In our paper, we derive a family of partial …

The relations between wavelet shrinkage and nonlinear diffusion for discontinuity-
preserving signal denoising are fairly well-understood for singlescale wavelet shrinkage, but …

Wavelet/frame shrinkage and nonlinear diffusion filtering are two popular methods for signal
and image denoising. The relationship between these two methods has been studied …

Diffusion processes driven by anisotropic diffusion tensors are known to be well-suited for
structure-preserving denoising. However, numerical implementations based on finite …

Following the recent work of JF. Aujol and A. Chambolle, a decomposition model of
grayscale images into three components (geometrical, texture and noise) has recently been …