Mixed noise pollution in HSI severely disturbs subsequent interpretations and applications.
In this technical review, we first give the noise analysis in different noisy HSIs and conclude …

Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing
(RS) imaging has provided a significant amount of spatial and spectral information for the …

Recent studies have shown that deep networks can achieve promising results for image
denoising. However, how to simultaneously incorporate the valuable achievements of …

Hyperspectral (HS) imaging, also known as image spectrometry, is a landmark technique in
geoscience and remote sensing (RS). In the past decade, enormous efforts have been made …

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 …

Recently, tensor Singular Value Decomposition (t-SVD)-based low-rank tensor completion
(LRTC) has achieved unprecedented success in addressing various pattern analysis issues …

Real-world image noise removal is a long-standing yet very challenging task in computer
vision. The success of deep neural network in denoising stimulates the research of noise …

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 …

Non-local low-rank tensor approximation has been developed as a state-of-the-art method
for hyperspectral image (HSI) restoration, which includes the tasks of denoising …