Hyperspectral image denoising: From model-driven, data-driven, to model-data-driven
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 …
In this technical review, we first give the noise analysis in different noisy HSIs and conclude …
Image restoration for remote sensing: Overview and toolbox
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 …
either active (eg, radar and lidar) or passive (eg, multispectral and hyperspectral) modes …
Non-local meets global: An iterative paradigm for hyperspectral image restoration
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 …
for hyperspectral image (HSI) restoration, which includes the tasks of denoising …
Cooperated spectral low-rankness prior and deep spatial prior for HSI unsupervised 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. However, there are pros and cons in both model …
Hyperspectral image restoration via total variation regularized low-rank tensor decomposition
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 …
during the acquisition process, eg, Gaussian noise, impulse noise, dead lines, stripes, etc …
HSI-DeNet: Hyperspectral image restoration via convolutional neural network
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 …
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
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 …
has obtained promising results in hyperspectral image (HSI) denoising. However, the …
Hyperspectral image denoising with total variation regularization and nonlocal low-rank tensor decomposition
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 …
which degrades the quality of the acquired image and limits the subsequent application. In …
Guaranteed tensor recovery fused low-rankness and smoothness
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 …
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
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 …
stripes, deadlines, impulse noise, Gaussian noise, and so on, which significantly limits their …