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

The tensor–tensor product-induced tensor nuclear norm (t-TNN)(Lu et al., 2020)
minimization for low-tubal-rank tensor recovery attracts broad attention recently. However …

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 …

Low-rank tensor recovery (LRTR) is a natural extension of low-rank matrix recovery (LRMR)
to high-dimensional arrays, which aims to reconstruct an underlying tensor from incomplete …

The robust tensor recovery problem consists in reconstructing a tensor from a sample of
entries corrupted by noise, which has attracted great interest in a wide range of practical …

Currently, low-rank tensor recovery employing the subspace prior information is an
emerging topic, which has attracted considerable attention. However, existing studies …

Currently, robust low-rank tensor reconstruction based on tensor singular value
decomposition (t-SVD) has made remarkable achievements in the fields of computer vision …

Recently, the transform-based tensor nuclear norm methods have achieved encouraging
results for low-rank tensor completion (LRTC) under the tensor singular value …

This paper considers the problem of recovering a tensor with an underlying low-tubal-rank
structure from a small number of corrupted linear measurements. Traditional approaches …

Low-rank tensor recovery (LRTR)[1] is a natural higherorder generalization of the
compressed sensing (CS)[2] and the low-rank matrix recovery (LRMR)[3, 4]. It has been …