Fusion from a spatially low resolution hyperspectral image (LR-HSI) and a spectrally low
resolution multispectral image (MSI) to produce a high spatial-spectral HSI (HR-HSI), known …

Low-rank modeling has achieved great success in visual data completion. However, the low-
rank assumption of original visual data may be in approximate mode, which leads to …

Motivated by the superior performance of nonconvex nonsmooth L p (0< p< 1) norm, this
paper introduces a novel method that combines the weighted Schatten p-norm, L p-norm …

Tensor singular value decomposition (t-SVD) has recently become increasingly popular for
tensor recovery under partial and/or corrupted observations. However, the existing-SVD …

For the ill-posed tensor completion (TC) problem, how to appropriately explore the hidden
information is a pivotal yet challenging issue. Two well-known priors have been intensively …

The low-dimensional manifold of image patches has been introduced as regularizer term,
and shown effective in hyperspectral image inpainting. However, in this article, we find that …

Hyperspectral image (HSI) completion is a fundamental problem in image processing and
remote sensing. Typical methods, either perform suboptimally due to lack of appropriate …

Image inpainting is a challenging problem with a wide range of applications such as object
removal and old photo restoration. The methods based on low-rank sparse prior have been …

In recent years, low-rank representation (LRR) has received increasing attention on
subspace clustering. Due to inevitable matrix inversion and singular value decomposition in …

Abstract Recently, tensor completion (TC), namely high-order extension of matrix
completion, has aroused widespread attention. Many priors have been investigated turning …