This paper reviews well-known classic algorithms and more recent experimental
approaches for distinguishing the weak signal of a target (either known or anomalous) from …

At the crossing of the statistical and functional analysis, there exists a relentless quest for an
efficient image denoising algorithm. In terms of greyscale imaging, a plethora of denoising …

This paper reviews the state-of-the-art in denoising methods for biological microscopy
images and introduces a new and original sparsity-based algorithm. The proposed method …

Removal of noise from an image is an extensively studied problem in image processing.
Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led …

Image denoising—removal of additive white Gaussian noise from an image—is one of the
oldest and most studied problems in image processing. Extensive work over several …

Model-based reconstruction is a powerful framework for solving a variety of inverse
problems in imaging. In recent years, enormous progress has been made in the problem of …

Low rank matrix approximation (LRMA), which aims to recover the underlying low rank
matrix from its degraded observation, has a wide range of applications in computer vision …

Many material and biological samples in scientific imaging are characterized by nonlocal
repeating structures. These are studied using scanning electron microscopy and electron …

In this article, the author presents a practical and accessible framework to understand some
of the basic underpinnings of these methods, with the intention of leading the reader to a …

Through exploiting the image nonlocal self-similarity (NSS) prior by clustering similar
patches to construct patch groups, recent studies have revealed that structural sparse …