The tensor tubal rank, defined based on the tensor singular value decomposition (t-SVD),
has obtained promising results in hyperspectral image (HSI) denoising. However, the …

Multispectral image (MSI) destri** is a challenging topic and has been attracting much
research attention in remote sensing area due to its importance in improving the image …

Recently, the method called tensor completion by parallel matrix factorization via tensor train
(TMac-TT) has achieved promising performance on estimating the missing information …

Speckle noise contamination is a common issue in ultrasound imaging system. Due to the
edge-preserving feature, total variation (TV) regularization-based techniques have been …

Low-rank modeling has achieved great success in tensor completion. However, the low-rank
prior is not sufficient for the recovery of the underlying tensor, especially when the sampling …

In this paper, we propose a novel tensor completion model using framelet regularization and
low-rank matrix factorization. An effective block successive upper-bound minimization …

Ptychography has risen as a reference X-ray imaging technique: it achieves resolutions of
one billionth of a meter, macroscopic field of view, or the capability to retrieve chemical or …

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 …

Synthetic aperture radar (SAR) imagery can provide useful information in a multitude of
applications, including climate change, environmental monitoring, meteorology, high …

The low-rank tensor factorization (LRTF) technique has received increasing attention in
many computer vision applications. Compared with the traditional matrix factorization …