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 image (HSI) denoising plays an important role in image quality improvement
and related applications. Convolutional neural network (CNN)-based image denoising …

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

Hyperspectral image super-resolution by fusing high-resolution multispectral image (HR-
MSI) and low-resolution hyperspectral image (LR-HSI) aims at reconstructing high resolution …

Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise
during the acquisition process, eg, Gaussian noise, impulse noise, dead lines, stripes, etc …

The spectral and the spatial information in hyperspectral images (HSIs) are the two sides of
the same coin. How to jointly model them is the key issue for HSIs' noise removal, including …

Recently, combining a low spatial resolution hyperspectral image (LR-HSI) with a high
spatial resolution multispectral image (HR-MSI) into an HR-HSI has become a popular …

In this article, we propose an alternating directional 3-D quasi-recurrent neural network for
hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge …