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

This paper addresses the recent advancements and trends in the field of
electroencephalography (EEG) using inverse problem solutions. Using the EEG data of the …

This letter proposes to estimate low-rank matrices by formulating a convex optimization
problem with nonconvex regularization. We employ parameterized nonconvex penalty …

The compressed sensing (CS) technique has been employed to reconstruct CT/CBCT
images from fewer projections as it is designed to recover a sparse signal from highly under …

Nonlocal total variation (NLTV) has emerged as a useful tool in variational methods for
image recovery problems. In this paper, we extend the NLTV-based regularization to …

Estimation of the regularization parameter, which strikes a balance between the data fidelity
and regularity, is essential for successfully solving ill-posed image restoration problems …

The restoration of images degraded by blurring and noise is one of the most important tasks
in image processing. In this paper, based on the total variation (TV) we propose a new …

Plug-and-play prirs (PnP) is an image reconstruction framework that uses an image
denoiser as an imaging prior. Unlike traditional regularized inversion, PnP does not require …

One of the technical challenges in cine magnetic resonance imaging (MRI) is to reduce the
acquisition time to enable the high spatio-temporal resolution imaging of a cardiac volume …

Low-dose computed tomography (CT) imaging without sacrifice of clinical tasks is desirable
due to the growing concerns about excessive radiation exposure to the patients. One …