Fast hyperspectral image denoising and inpainting based on low-rank and sparse representations
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 …
Nonlocal low-rank regularized tensor decomposition for hyperspectral image denoising
Hyperspectral image (HSI) enjoys great advantages over more traditional image types for
various applications due to the extra knowledge available. For the nonideal optical and …
various applications due to the extra knowledge available. For the nonideal optical and …
Hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation
Noise reduction is an active research area in image processing due to its importance in
improving the quality of image for object detection and classification. In this paper, we …
improving the quality of image for object detection and classification. In this paper, we …
Sparse transfer manifold embedding for hyperspectral target detection
Target detection is one of the most important applications in hyperspectral remote sensing
image analysis. However, the state-of-the-art machine-learning-based algorithms for …
image analysis. However, the state-of-the-art machine-learning-based algorithms for …
Survey of hyperspectral image denoising methods based on tensor decompositions
T Lin, S Bourennane - EURASIP journal on Advances in Signal …, 2013 - Springer
A hyperspectral image (HSI) is always modeled as a three-dimensional tensor, with the first
two dimensions indicating the spatial domain and the third dimension indicating the spectral …
two dimensions indicating the spatial domain and the third dimension indicating the spectral …
Multiple-spectral-band CRFs for denoising junk bands of hyperspectral imagery
P Zhong, R Wang - IEEE Transactions on Geoscience and …, 2012 - ieeexplore.ieee.org
Denoising of hyperspectral imagery in the domain of imaging spectroscopy by conditional
random fields (CRFs) is addressed in this work. For denoising of hyperspectral imagery, the …
random fields (CRFs) is addressed in this work. For denoising of hyperspectral imagery, the …
A comparative study on linear regression-based noise estimation for hyperspectral imagery
In the traditional signal model, signal is assumed to be deterministic, and noise is assumed
to be random, additive and uncorrelated to the signal component. A hyperspectral image …
to be random, additive and uncorrelated to the signal component. A hyperspectral image …
Hyperspectral image denoising with a spatial–spectral view fusion strategy
In this paper, we propose a hyperspectral image denoising algorithm with a Spatial-spectral
view fusion strategy. The idea is to denoise a noisy hyperspectral 3-D cube using the …
view fusion strategy. The idea is to denoise a noisy hyperspectral 3-D cube using the …
Hyperspectral airborne “Viareggio 2013 Trial” data collection for detection algorithm assessment
For many years, the entire target detection scientific community has felt the urge for fully
ground-truthed hyperspectral imagery data sets expressly released for testing and …
ground-truthed hyperspectral imagery data sets expressly released for testing and …
Poissonian blurred hyperspectral imagery denoising based on variable splitting and penalty technique
Poisson noise is one of the significant sources of noise present in hyperspectral imagery
(HSI). In most of the existing denoising methods, Poisson noise is first transformed into …
(HSI). In most of the existing denoising methods, Poisson noise is first transformed into …