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 …

The linear inverse problem is fundamental to the development of various scientific areas.
Innumerable attempts have been carried out to solve different variants of the linear inverse …

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 …

Model-driven methods and data-driven methods have been widely developed for
hyperspectral image (HSI) denoising. However, there are pros and cons in both model …

Hyperspectral image (HSI) denoising is an ill-posed inverse problem. The underlying
physical model is always important to tackle this problem, which is unfortunately ignored by …

Hyperspectral imaging offers new perspectives for diverse applications, ranging from the
monitoring of the environment using airborne or satellite remote sensing, precision farming …

As a spotlighted nonlocal image representation model, group sparse representation (GSR)
has demonstrated a great potential in diverse image restoration tasks. Most of the existing …

Recently, deep-learning-based denoising methods for hyperspectral images (HSIs) have
been comprehensively studied and achieved impressive performance because they can …

The ability to capture fine spectral discriminative information enables hyperspectral images
(HSIs) to observe, detect and identify objects with subtle spectral discrepancy. However, the …

Hyperspectral image (HSI) restoration is to recover the clean image from degraded version,
such as the noisy, blurred, or damaged. Recent low-rank tensor-based recovery methods …