Nonlocal Tensor-Ring Decomposition for Hyperspectral Image Denoising

Hyperspectral image (HSI) denoising is a fundamental problem in remote sensing and image processing. Recently, nonlocal low-rank tensor approximation-based denoising methods have attracted much attention due to their advantage of being capable of fully exploiting the nonlocal self-similarity and glo...

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Vydáno v:	IEEE transactions on geoscience and remote sensing Ročník 58; číslo 2; s. 1348 - 1362
Hlavní autoři:	Chen, Yong, He, Wei, Yokoya, Naoto, Huang, Ting-Zhu, Zhao, Xi-Le
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.02.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Approximation Computer simulation Correlation Data models Datasets Decomposition Denoising Dimensions hyperspectral image (HSI) Hyperspectral imaging Image processing Matrix decomposition Methods Noise levels Noise reduction nonlocal self-similarity Optimization Performance evaluation Remote sensing Self-similarity Spectral correlation tensor-ring (TR) decomposition Tensors
ISSN:	0196-2892, 1558-0644
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Abstract	Hyperspectral image (HSI) denoising is a fundamental problem in remote sensing and image processing. Recently, nonlocal low-rank tensor approximation-based denoising methods have attracted much attention due to their advantage of being capable of fully exploiting the nonlocal self-similarity and global spectral correlation. Existing nonlocal low-rank tensor approximation methods were mainly based on two common decomposition [Tucker or CANDECOMP/PARAFAC (CP)] methods and achieved the state-of-the-art results, but they are subject to certain issues and do not produce the best approximation for a tensor. For example, the number of parameters for Tucker decomposition increases exponentially according to its dimensions, and CP decomposition cannot better preserve the intrinsic correlation of the HSI. In this article, a novel nonlocal tensor-ring (TR) approximation is proposed for HSI denoising by using TR decomposition to explore the nonlocal self-similarity and global spectral correlation simultaneously. TR decomposition approximates a high-order tensor as a sequence of cyclically contracted third-order tensors, which has strong ability to explore these two intrinsic priors and to improve the HSI denoising results. Moreover, an efficient proximal alternating minimization algorithm is developed to optimize the proposed TR decomposition model efficiently. Extensive experiments on three simulated data sets under several noise levels and two real data sets verify that the proposed TR model provides better HSI denoising results than several state-of-the-art methods in terms of quantitative and visual performance evaluations.
AbstractList	Hyperspectral image (HSI) denoising is a fundamental problem in remote sensing and image processing. Recently, nonlocal low-rank tensor approximation-based denoising methods have attracted much attention due to their advantage of being capable of fully exploiting the nonlocal self-similarity and global spectral correlation. Existing nonlocal low-rank tensor approximation methods were mainly based on two common decomposition [Tucker or CANDECOMP/PARAFAC (CP)] methods and achieved the state-of-the-art results, but they are subject to certain issues and do not produce the best approximation for a tensor. For example, the number of parameters for Tucker decomposition increases exponentially according to its dimensions, and CP decomposition cannot better preserve the intrinsic correlation of the HSI. In this article, a novel nonlocal tensor-ring (TR) approximation is proposed for HSI denoising by using TR decomposition to explore the nonlocal self-similarity and global spectral correlation simultaneously. TR decomposition approximates a high-order tensor as a sequence of cyclically contracted third-order tensors, which has strong ability to explore these two intrinsic priors and to improve the HSI denoising results. Moreover, an efficient proximal alternating minimization algorithm is developed to optimize the proposed TR decomposition model efficiently. Extensive experiments on three simulated data sets under several noise levels and two real data sets verify that the proposed TR model provides better HSI denoising results than several state-of-the-art methods in terms of quantitative and visual performance evaluations.
Author	Yokoya, Naoto He, Wei Chen, Yong Zhao, Xi-Le Huang, Ting-Zhu
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Cites_doi	10.1109/TGRS.2012.2227764 10.1109/TGRS.2017.2771155 10.1109/JSTARS.2018.2791718 10.1109/TCYB.2019.2936042 10.1109/CVPR.2017.625 10.1109/TGRS.2016.2547879 10.1137/060670080 10.1109/TGRS.2019.2924017 10.1016/j.apm.2019.02.001 10.1109/CVPR.2014.377 10.1109/MGRS.2016.2637824 10.1109/TGRS.2018.2859203 10.1109/TIP.2018.2839531 10.1109/CVPR.2019.00703 10.1016/j.sigpro.2017.06.012 10.1109/TGRS.2014.2363101 10.1016/j.isprsjprs.2019.09.003 10.1109/TGRS.2012.2187063 10.1137/07070111X 10.1109/TIP.2017.2676466 10.1109/TIP.2003.819861 10.1109/TGRS.2015.2452812 10.1007/s10107-011-0484-9 10.1109/TPAMI.2017.2734888 10.1137/120891009 10.1016/j.cam.2019.06.004 10.1109/JSTARS.2017.2779539 10.1109/LGRS.2017.2701805 10.1609/aaai.v33i01.33019151 10.1109/JSTARS.2018.2877722 10.1109/TGRS.2012.2185054 10.1109/CVPR.2005.38 10.1109/TGRS.2013.2284280 10.1109/TCYB.2017.2677944 10.1109/TIP.2012.2210725 10.1016/j.apm.2018.06.044 10.1109/TIP.2012.2235847 10.1109/TGRS.2018.2889256 10.23919/APSIPA.2018.8659708 10.1109/TIP.2007.901238 10.1109/TGRS.2018.2865197 10.1109/CVPR.2016.187 10.1016/j.ins.2018.01.035 10.1561/2400000003 10.1109/TGRS.2017.2706326 10.1016/0167-2789(92)90242-F 10.1109/ICCV.2017.607 10.1109/TGRS.2014.2321557 10.1109/ICASSP.2019.8682231 10.1109/JSTARS.2018.2805290 10.1109/CVPR.2014.366 10.1109/JSTARS.2018.2796570 10.1109/TIP.2006.881969 10.1109/LGRS.2008.915736 10.1109/TGRS.2018.2833473 10.1109/TIP.2011.2109730 10.1109/TGRS.2015.2489218
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Snippet	Hyperspectral image (HSI) denoising is a fundamental problem in remote sensing and image processing. Recently, nonlocal low-rank tensor approximation-based...
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SubjectTerms	Algorithms Approximation Computer simulation Correlation Data models Datasets Decomposition Denoising Dimensions hyperspectral image (HSI) Hyperspectral imaging Image processing Matrix decomposition Methods Noise levels Noise reduction nonlocal self-similarity Optimization Performance evaluation Remote sensing Self-similarity Spectral correlation tensor-ring (TR) decomposition Tensors
Title	Nonlocal Tensor-Ring Decomposition for Hyperspectral Image Denoising
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