The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Imagens hiperespectrais (HSIs) são geralmente suscetíveis a vários ruídos, como ruído gaussiano e de faixa. Recentemente, vários algoritmos de remoção de ruído foram propostos para recuperar os HSIs. No entanto, essas abordagens não podem usar informações espectrais de forma eficiente e sofrem com a fraqueza da remoção de ruído de faixa. Aqui, propomos um método de decomposição tensorial com duas restrições diferentes para remover o ruído misto dos HSIs. Para um cubo HSI, primeiro empregamos a decomposição de valor singular do tensor (t-SVD) para preservar efetivamente as informações de baixa classificação dos HSIs. Considerando a propriedade de continuidade dos espectros HSIs, projetamos uma restrição de suavidade simples usando a regularização de Tikhonov para decomposição de tensores para melhorar o desempenho de remoção de ruído. Além disso, também projetamos uma nova restrição unidirecional de variação total (TV) para filtrar o ruído de faixa dos HSIs. Esta estratégia alcançará melhor desempenho na preservação dos detalhes das imagens do que os modelos de TV originais. O método desenvolvido é avaliado em HSIs ruidosos sintéticos e reais e mostra resultados favoráveis.
Zhen LI
Beijing Institute of Technology
Baojun ZHAO
Beijing Institute of Technology
Wenzheng WANG
Beijing Institute of Technology,Peking University
Baoxian WANG
Shijiazhuang
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Zhen LI, Baojun ZHAO, Wenzheng WANG, Baoxian WANG, "Hyperspectral Image Denoising Using Tensor Decomposition under Multiple Constraints" in IEICE TRANSACTIONS on Fundamentals,
vol. E104-A, no. 6, pp. 949-953, June 2021, doi: 10.1587/transfun.2020EAL2099.
Abstract: Hyperspectral images (HSIs) are generally susceptible to various noise, such as Gaussian and stripe noise. Recently, numerous denoising algorithms have been proposed to recover the HSIs. However, those approaches cannot use spectral information efficiently and suffer from the weakness of stripe noise removal. Here, we propose a tensor decomposition method with two different constraints to remove the mixed noise from HSIs. For a HSI cube, we first employ the tensor singular value decomposition (t-SVD) to effectively preserve the low-rank information of HSIs. Considering the continuity property of HSIs spectra, we design a simple smoothness constraint by using Tikhonov regularization for tensor decomposition to enhance the denoising performance. Moreover, we also design a new unidirectional total variation (TV) constraint to filter the stripe noise from HSIs. This strategy will achieve better performance for preserving images details than original TV models. The developed method is evaluated on both synthetic and real noisy HSIs, and shows the favorable results.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020EAL2099/_p
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@ARTICLE{e104-a_6_949,
author={Zhen LI, Baojun ZHAO, Wenzheng WANG, Baoxian WANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Hyperspectral Image Denoising Using Tensor Decomposition under Multiple Constraints},
year={2021},
volume={E104-A},
number={6},
pages={949-953},
abstract={Hyperspectral images (HSIs) are generally susceptible to various noise, such as Gaussian and stripe noise. Recently, numerous denoising algorithms have been proposed to recover the HSIs. However, those approaches cannot use spectral information efficiently and suffer from the weakness of stripe noise removal. Here, we propose a tensor decomposition method with two different constraints to remove the mixed noise from HSIs. For a HSI cube, we first employ the tensor singular value decomposition (t-SVD) to effectively preserve the low-rank information of HSIs. Considering the continuity property of HSIs spectra, we design a simple smoothness constraint by using Tikhonov regularization for tensor decomposition to enhance the denoising performance. Moreover, we also design a new unidirectional total variation (TV) constraint to filter the stripe noise from HSIs. This strategy will achieve better performance for preserving images details than original TV models. The developed method is evaluated on both synthetic and real noisy HSIs, and shows the favorable results.},
keywords={},
doi={10.1587/transfun.2020EAL2099},
ISSN={1745-1337},
month={June},}
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TY - JOUR
TI - Hyperspectral Image Denoising Using Tensor Decomposition under Multiple Constraints
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 949
EP - 953
AU - Zhen LI
AU - Baojun ZHAO
AU - Wenzheng WANG
AU - Baoxian WANG
PY - 2021
DO - 10.1587/transfun.2020EAL2099
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E104-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2021
AB - Hyperspectral images (HSIs) are generally susceptible to various noise, such as Gaussian and stripe noise. Recently, numerous denoising algorithms have been proposed to recover the HSIs. However, those approaches cannot use spectral information efficiently and suffer from the weakness of stripe noise removal. Here, we propose a tensor decomposition method with two different constraints to remove the mixed noise from HSIs. For a HSI cube, we first employ the tensor singular value decomposition (t-SVD) to effectively preserve the low-rank information of HSIs. Considering the continuity property of HSIs spectra, we design a simple smoothness constraint by using Tikhonov regularization for tensor decomposition to enhance the denoising performance. Moreover, we also design a new unidirectional total variation (TV) constraint to filter the stripe noise from HSIs. This strategy will achieve better performance for preserving images details than original TV models. The developed method is evaluated on both synthetic and real noisy HSIs, and shows the favorable results.
ER -