AH igh-D imensional and I ncomplete (HDI) tensor is frequently encountered in a big data-
related application concerning the complex dynamic interactions among numerous entities …

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 …

Fusing a pair of low-spatial-resolution hyperspec-tral image (LR-HSI) and high-spatial-
resolution multispectral image (HR-MSI) has been regarded as an effective and economical …

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 …

Recently, transform-based tensor nuclear norm (TNN) minimization methods have received
increasing attention for recovering third-order tensors in multi-dimensional imaging …

Multi-dimensional image recovery from incomplete data is a fundamental problem in data
processing. Due to its advantage of capturing the correlations between any modes of the …

Since higher-order tensors are naturally suitable for representing multi-dimensional data in
real-world, eg, color images and videos, low-rank tensor representation has become one of …

Recent research has demonstrated that low tubal rank recovery based on tensor has
received extensive attention. In this correspondence, we define tensor double nuclear norm …

The tensor nuclear norm (TNN), defined as the sum of nuclear norms of frontal slices of the
tensor in a frequency domain, has been found useful in solving low-rank tensor recovery …