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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
초분광 이미지(HSI)는 일반적으로 가우스 및 줄무늬 노이즈와 같은 다양한 노이즈에 취약합니다. 최근 HSI를 복구하기 위해 수많은 잡음 제거 알고리즘이 제안되었습니다. 그러나 이러한 접근 방식은 스펙트럼 정보를 효율적으로 사용할 수 없으며 스트라이프 노이즈 제거의 약점이 있습니다. 여기서는 HSI에서 혼합 잡음을 제거하기 위해 두 가지 다른 제약 조건을 갖는 텐서 분해 방법을 제안합니다. HSI 큐브의 경우 먼저 HSI의 하위 순위 정보를 효과적으로 보존하기 위해 t-SVD(텐서 특이값 분해)를 사용합니다. HSI 스펙트럼의 연속성 속성을 고려하여 잡음 제거 성능을 향상시키기 위해 텐서 분해를 위한 Tikhonov 정규화를 사용하여 간단한 평활도 제약 조건을 설계합니다. 또한 HSI의 스트라이프 노이즈를 필터링하기 위해 새로운 단방향 TV(Total Variation) 제약 조건도 설계했습니다. 이 전략은 원래 TV 모델보다 이미지 세부 사항을 보존하는 데 더 나은 성능을 달성합니다. 개발된 방법은 합성 및 실제 잡음이 있는 HSI 모두에서 평가되었으며 유리한 결과를 보여줍니다.
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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부
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
부
@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},}
부
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 -