A tensor, represented as a multidimensional array, has crucial applications in various fields
such as image processing and high-dimensional data mining. This study defines a novel …

Recently, tensor Singular Value Decomposition (t-SVD)-based low-rank tensor completion
(LRTC) has achieved unprecedented success in addressing various pattern analysis issues …

Tensor recovery is a fundamental problem in tensor research field. It generally requires to
explore intrinsic prior structures underlying tensor data, and formulate them as certain forms …

The main aim of this paper is to develop a framelet representation of the tensor nuclear norm
for third-order tensor recovery. In the literature, the tensor nuclear norm can be computed by …

Rapid advances in sensor, wireless communication, cloud computing and data science
have brought unprecedented amount of data to assist transportation engineers and …

Low-rank tensor completion aims to recover the missing entries of multi-way data, which has
become popular and vital in many fields such as signal processing and computer vision. It …

Inverse problems in multi-dimensional imaging, eg, completion, denoising, and compressive
sensing, are challenging owing to the big volume of the data and the inherent ill-posedness …

High-order tensors, as a powerful tool for representation of higher-order data, have garnered
much attention across various applications including image data, data mining, and big data …

Infrared (IR) small target detection under complex environments is an essential part of IR
search and track systems. However, previously proposed IR small target detection …

A plethora of previous studies indicates that making full use of multifarious intrinsic
properties of primordial data is a valid pathway to recover original images from their …