TRANSFORM

An overview of state-of-the-art image restoration in electron microscopy

In Life Science research, electron microscopy (EM) is an essential tool for morphological analysis at the subcellular level as it allows for visualization at nanometer resolution. However, electron micrographs contain image degradations such as noise …

Sparse recovery in magnetic resonance imaging with a Markov random field prior

Iterative CT reconstruction using shearlet-based regularization

Total variation (TV) methods have been proposed to improve the image quality in count-reduced images, by reducing the variation between neighboring pixels. Although very easy to implement and fast to compute, TV-based methods may lead to a loss of …

Augmented Lagrangian based reconstruction of non-uniformly sub-Nyquist sampled MRI data

A recursive scheme for computing autocorrelation functions of decimated complex wavelet subbands

This paper deals with the problem of the exact computation of the autocorrelation function of a real or complex discrete wavelet subband of a signal, when the autocorrelation function (or Power Spectral Density, PSD) of the signal in the time domain …

COMPASS: a joint framework for parallel imaging and compressive sensing in MRI

Parallel Imaging MRI (pMRI) and Compressive Sensing (CS) are two reconstruction techniques that have recently been applied to increase MRI performance. In this paper we demonstrate that a combined analysis of the pMRI and CS problems leads to a …

Removal of correlated noise by modeling the signal of interest in the wavelet domain

Images, captured with digital imaging devices, often contain noise. In literature, many algorithms exist for the removal of white uncorrelated noise, but they usually fail when applied to images with correlated noise. In this paper, we design a new …