Hyperspectral image denoising via global spatial-spectral total variation regularized nonconvex local low-rank tensor approximation
Hyperspectral image (HSI) denoising aims to restore clean HSI from the noise-contaminated
one which is usually caused during data acquisition and conversion. In this paper, we
propose a novel spatial-spectral total variation (SSTV) regularized nonconvex local low-rank
(LR) tensor approximation method to remove mixed noise in HSIs. From one aspect, the
clean HSI data have its underlying local LR tensor property, even though the real HSI data is
not globally low-rank due to the non-independent and non-identically distributed noise and�…
one which is usually caused during data acquisition and conversion. In this paper, we
propose a novel spatial-spectral total variation (SSTV) regularized nonconvex local low-rank
(LR) tensor approximation method to remove mixed noise in HSIs. From one aspect, the
clean HSI data have its underlying local LR tensor property, even though the real HSI data is
not globally low-rank due to the non-independent and non-identically distributed noise and�…
Hyperspectral Image Denoising via Global Spatial-Spectral Total Variation Regularized Nonconvex Local Low-Rank Tensor Approximation
H Zeng, X Xie, J Ning�- arXiv preprint arXiv:2006.00235, 2020 - arxiv.org
Hyperspectral image (HSI) denoising aims to restore clean HSI from the noise-contaminated
one. Noise contamination can often be caused during data acquisition and conversion. In
this paper, we propose a novel spatial-spectral total variation (SSTV) regularized nonconvex
local low-rank (LR) tensor approximation method to remove mixed noise in HSIs. From one
aspect, the clean HSI data have its underlying local LR tensor property, even though the real
HSI data may not be globally low-rank due to out-liers and non-Gaussian noise. According�…
one. Noise contamination can often be caused during data acquisition and conversion. In
this paper, we propose a novel spatial-spectral total variation (SSTV) regularized nonconvex
local low-rank (LR) tensor approximation method to remove mixed noise in HSIs. From one
aspect, the clean HSI data have its underlying local LR tensor property, even though the real
HSI data may not be globally low-rank due to out-liers and non-Gaussian noise. According�…
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