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 …

Hyperspectral images (HSIs) with high spectral resolution only have the low spatial
resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution …

The performance of deep learning-based denoisers highly depends on the quantity and
quality of training data. However, paired noisy–clean training images are generally …

The decrease in the widths of spectral bands in hyperspectral imaging leads to a decrease
in signal-to-noise ratio (SNR) of measurements. The decreased SNR reduces the reliability …

Hyperspectral image (HSI) super-resolution is a hot topic in remote sensing and computer
vision. Recently, tensor analysis has been proven to be an efficient technology for HSI …

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 …

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 …

In general, hyperspectral images (HSIs) are degraded by a mixture of complicated noise (ie,
mixture of Gaussian and sparse noise), and how to precisely model HSI noise plays a vital …

Tensor-ring (TR) decomposition has recently attracted considerable attention in solving the
low-rank tensor completion (LRTC) problem. However, due to an unbalanced unfolding …

In this article, we propose a novel tensor learning and coding model for third-order data
completion. The aim of our model is to learn a data-adaptive dictionary from given …