This paper presents an extension of the Non-Local Means denoising method, that effectively
exploits the affine invariant self-similarities present in the images of real scenes. Our method …

The weighted nuclear norm minimization has been widely used in low-level vision tasks. To
treat different singular values more flexibly, in this paper, we adopt the smoothly clipped …

We propose a novel variational method for Rician noise removal in magnitude-based
magnetic resonance (MR) imaging. We first explore the link between the Gaussian noise …

Modeling magnitude magnetic resonance images (MRI) Rician denoising in a Bayesian or
generalized Tikhonov framework using total variation (TV) leads naturally to the …

A bayesian model for Diffusion Tensorial Magnetic Resonance Images denosing and
reconstruction is considered. This is based on a Tikhonov like-functional for Total …

Recent research on magnitude Magnetic Resonance Images (MRI) reconstruction from the
Fourier inverse transform of complex (gaussian contaminated) data sets focuses on the …

Image comparison is a main ingredient in many image processing and computer vision
problems and applications, and not surprisingly it is a very diverse topic. The subject of this …

In this work we consider the numerical resolution of a variational Rician denoising model
recently proposed for Magnetic Resonance Images (MRI) restoration. Two new different …

In this work we tackle the problem of Magnetic Resonance Images (MRI) Rician denoising to
enhance Diffusion Tensor Image (DTI) reconstruction. In a variational framework based on …

Medical image processing is an interdisciplinary research field attracting expertise from
applied mathematics, computer sciences, engineering, statistics, physics, biology and …