Fast hyperspectral image denoising and inpainting based on low-rank and sparse representations
L Zhuang, JM Bioucas-Dias - IEEE Journal of Selected Topics …, 2018 - ieeexplore.ieee.org
This paper introduces two very fast and competitive hyperspectral image (HSI) restoration
algorithms: fast hyperspectral denoising (FastHyDe), a denoising algorithm able to cope with …
algorithms: fast hyperspectral denoising (FastHyDe), a denoising algorithm able to cope with …
Hyperspectral image denoising based on global and nonlocal low-rank factorizations
The ever-increasing spectral resolution of hyperspectral images (HSIs) is often obtained at
the cost of a decrease in the signal-to-noise ratio of the measurements, thus calling for …
the cost of a decrease in the signal-to-noise ratio of the measurements, thus calling for …
Hyperspectral image denoising and anomaly detection based on low-rank and sparse representations
Hyperspectral imaging measures the amount of electromagnetic energy across the
instantaneous field of view at a very high resolution in hundreds or thousands of spectral …
instantaneous field of view at a very high resolution in hundreds or thousands of spectral …
Block-matching convolutional neural network for image denoising
B Ahn, NI Cho - arXiv preprint arXiv:1704.00524, 2017 - arxiv.org
There are two main streams in up-to-date image denoising algorithms: non-local self
similarity (NSS) prior based methods and convolutional neural network (CNN) based …
similarity (NSS) prior based methods and convolutional neural network (CNN) based …
Adaptive hyperspectral mixed noise removal
This article proposes a new denoising method for hyperspectral images (HSIs) corrupted by
mixtures (in a statistical sense) of stripe noise, Gaussian noise, and impulsive noise. The …
mixtures (in a statistical sense) of stripe noise, Gaussian noise, and impulsive noise. The …
Hyperspectral image denoising via low-rank representation and CNN denoiser
H Sun, M Liu, K Zheng, D Yang, J Li… - IEEE Journal of Selected …, 2021 - ieeexplore.ieee.org
Hyperspectral images (HSIs) are widely used in various tasks such as earth observation and
target detection. However, during the imaging process, HSIs are often corrupted by various …
target detection. However, during the imaging process, HSIs are often corrupted by various …
Image denoising using weighted nuclear norm minimization with multiple strategies
Low rank methods have shown to provide excellent denoising performance, of which
weighted nuclear norm minimization (WNNM) is particularly effective. It assigns different …
weighted nuclear norm minimization (WNNM) is particularly effective. It assigns different …
Hyperspectral image mixed denoising using difference continuity-regularized nonlocal tensor subspace low-rank learning
L Sun, C He - IEEE Geoscience and Remote Sensing Letters, 2021 - ieeexplore.ieee.org
With the rapid advancement of spectrometers, the imaging range of the electromagnetic
spectrum starts growing narrower. The reduction of electromagnetic wave energy received …
spectrum starts growing narrower. The reduction of electromagnetic wave energy received …
Block-matching convolutional neural network (BMCNN): improving CNN-based denoising by block-matched inputs
There are two main streams in up-to-date image denoising algorithms: non-local self
similarity (NSS) prior based methods and convolutional neural network (CNN) based …
similarity (NSS) prior based methods and convolutional neural network (CNN) based …
Projection domain processing for low-dose CT reconstruction based on subspace identification
J Ren, N Liang, X Yu, Y Wang, A Cai… - Journal of X-Ray …, 2023 - content.iospress.com
Purpose: Low-dose computed tomography (LDCT) has promising potential for dose
reduction in medical applications, while suffering from low image quality caused by noise …
reduction in medical applications, while suffering from low image quality caused by noise …