Existing methods for tensor completion (TC) have limited ability for characterizing low-rank
(LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes …

Recently, tensor sparsity modeling has achieved great success in the tensor completion
(TC) problem. In real applications, the sparsity of a tensor can be rationally measured by low …

Hyperspectral images (HSIs) are usually contaminated by various kinds of noise, such as
stripes, deadlines, impulse noise, Gaussian noise, and so on, which significantly limits their …

Multispectral images (MSI) can help deliver more faithful representation for real scenes than
the traditional image system, and enhance the performance of many computer vision tasks …

Recent low-rank based matrix/tensor recovery methods have been widely explored in
multispectral images (MSI) denoising. These methods, however, ignore the difference of the …

This paper addresses the problem of line pattern noise removal from a single image, such
as rain streak, hyperspectral stripe and so on. Most of the previous methods model the line …

Conventional tensor completion (TC) methods generally assume that the sparsity of tensor-
valued data lies in the global subspace. The so-called global sparsity prior is measured by …

High-dimensional image representation is a challenging task since data has the intrinsic low-
dimensional and shift-invariant characteristics. Currently, popular methods, such as tensor …

Multi-dimensional images, such as color images and multi-spectral images (MSIs), are
highly correlated and contain abundant spatial and spectral information. However, real …

Low-rankness has been widely exploited for the tensor completion problem. Recent
advances have suggested that the tensor nuclear norm often leads to a promising …