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 …

Considering the labeled samples may be difficult to obtain because they require human
annotators, special devices, or expensive and slow experiments. Semi-supervised learning …

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 …

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 …

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) mixed noise removal is a fundamental problem and an important
preprocessing step in remote sensing fields. The low-rank approximation-based methods …

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 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 …