The accuracy of the magnetic resonance (MR) image diagnosis depends on the quality of
the image, which degrades mainly due to noise and artifacts. The noise is introduced …

Recently, tensor train rank, defined by a well-balanced matricization scheme, has been
shown the powerful capacity to capture the hidden correlations among different modes of a …

Alzheimer's disease (AD) is a type of neurological disorder and is a most frequent cause of
dementia across the world. The area of medical imaging has created an advancement in …

In this paper, we propose a novel model for remote sensing images destri**, which
includes the Schatten 1∕ 2-norm and the unidirectional first-order and high-order total …

This work is the result of more than 10 years of research in the area of MRI from a signal and
noise perspective. Our interest has always been to properly model the noise that affects our …

In vivo fluorine‐19 MRI using F‐based tracer media has shown utility and versatility for a
wide range of biomedical uses, particularly immune and stem cell detection, as well as …

Maritime images captured under low-light imaging condition easily suffer from low visibility
and unexpected noise, leading to negative effects on maritime traffic supervision and …

The low-rank matrix approximation (LRMA) is an efficient image denoising method to reduce
additive Gaussian noise. However, the existing low-rank matrix approximation does not …

Diffusion magnetic resonance imaging (MRI) datasets suffer from low Signal-to-Noise Ratio
(SNR), especially at high b-values. Acquiring data at high b-values contains relevant …

In this paper, we propose a novel model to restore an image corrupted by blur and Cauchy
noise. The model is composed of a data fidelity term and two regularization terms including …