Hyperspectral image denoising: From model-driven, data-driven, to model-data-driven

Q Zhang, Y Zheng, Q Yuan, M Song… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
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 …

Image restoration for remote sensing: Overview and toolbox

B Rasti, Y Chang, E Dalsasso, L Denis… - IEEE Geoscience and …, 2021 - ieeexplore.ieee.org
Remote sensing provides valuable information about objects and areas from a distance in
either active (eg, radar and lidar) or passive (eg, multispectral and hyperspectral) modes …

Non-local meets global: An iterative paradigm for hyperspectral image restoration

W He, Q Yao, C Li, N Yokoya, Q Zhao… - … on Pattern Analysis …, 2020 - ieeexplore.ieee.org
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 …

Cooperated spectral low-rankness prior and deep spatial prior for HSI unsupervised denoising

Q Zhang, Q Yuan, M Song, H Yu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 restoration via total variation regularized low-rank tensor decomposition

Y Wang, J Peng, Q Zhao, Y Leung… - IEEE Journal of …, 2017 - ieeexplore.ieee.org
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 …

HSI-DeNet: Hyperspectral image restoration via convolutional neural network

Y Chang, L Yan, H Fang, S Zhong… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
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 …

Mixed noise removal in hyperspectral image via low-fibered-rank regularization

YB Zheng, TZ Huang, XL Zhao, TX Jiang… - … on Geoscience and …, 2019 - ieeexplore.ieee.org
The tensor tubal rank, defined based on the tensor singular value decomposition (t-SVD),
has obtained promising results in hyperspectral image (HSI) denoising. However, the …

Hyperspectral image denoising with total variation regularization and nonlocal low-rank tensor decomposition

H Zhang, L Liu, W He, L Zhang - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Hyperspectral images (HSIs) are normally corrupted by a mixture of various noise types,
which degrades the quality of the acquired image and limits the subsequent application. In …

Guaranteed tensor recovery fused low-rankness and smoothness

H Wang, J Peng, W Qin, J Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
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 …

Hyperspectral image denoising using local low-rank matrix recovery and global spatial–spectral total variation

W He, H Zhang, H Shen, L Zhang - IEEE Journal of Selected …, 2018 - ieeexplore.ieee.org
Hyperspectral images (HSIs) are usually contaminated by various kinds of noise, such as
stripes, deadlines, impulse noise, Gaussian noise, and so on, which significantly limits their …