Poisson observations for videos are important models in video processing and computer
vision. In this paper, we study the third-order tensor completion problem with Poisson …

Most of the existing analysis methods for tensors (or multi-way arrays) only assume that
tensors to be completed are of low rank. However, for example, when they are applied to …

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 …

Tensor completion is a fundamental tool for incomplete data analysis, where the goal is to
predict missing entries from partial observations. However, existing methods often make the …

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 …

Tensor train (TT) decomposition has drawn people's attention due to its powerful
representation ability and performance stability in high-order tensors. In this paper, we …

The problem of incomplete data is common in signal processing and machine learning.
Tensor completion algorithms aim to recover the incomplete data from its partially observed …

Tensor completion has been widely used in computer vision and machine learning. Most
existing tensor completion methods empirically assume the intrinsic tensor is simultaneous …

Tensor completion aims to recover missing entries given incomplete multi-dimensional data
by making use of the prior low-rank information, and has various applications because many …

Low-rank tensor completion plays an important role in many applications such as image
processing, computer vision, and machine learning. A widely used convex relaxation of this …