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 …

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 …

Hyperspectral image (HSI) denoising is challenging not only because of the difficulty in
preserving both spectral and spatial structures simultaneously, but also due to the …

Label information plays an important role in a supervised hyperspectral image classification
problem. However, current classification methods all ignore an important and inevitable …

Non-local low-rank tensor approximation has been developed as a state-of-the-art method
for hyperspectral image (HSI) denoising. Unfortunately, while their denoising performance …

Recent low-rank based matrix/tensor recovery methods have been widely explored in
multispectral images (MSI) denoising. These methods, however, ignore the difference of the …

This paper studies the hyperspectral image (HSI) denoising problem under the assumption
that the signal is low in rank. In this paper, a mixture of Gaussian noise and sparse noise is …

Many spectral unmixing approaches ranging from geometry, algebra to statistics have been
proposed, in which nonnegative matrix factorization (NMF)-based ones form an important …

Robust principal component analysis (RPCA) has been widely used for many data analysis
problems in matrix data. Robust tensor principal component analysis (RTPCA) aims to …

Synchrotron-based x-ray tomography is a noninvasive imaging technique that allows for
reconstructing the internal structure of materials at high spatial resolutions from tens of …