Infrared and visible image fusion based on multi‐channel convolutional neural network

Abstract For the lack of labels in infrared and visible image fusion network, an infrared and visible image fusion model based on multi‐channel unsupervised convolutional neural network (CNN) is proposed in this paper, in order to extract more detailed information through multi‐channel inputs. In co...
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Autor*in:	Hongmei Wang [verfasserIn] Wenbo An [verfasserIn] Lin Li [verfasserIn] Chenkai Li [verfasserIn] Daming Zhou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Übergeordnetes Werk:	In: IET Image Processing - Wiley, 2021, 16(2022), 6, Seite 1575-1584
Übergeordnetes Werk:	volume:16 ; year:2022 ; number:6 ; pages:1575-1584

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1049/ipr2.12431

Katalog-ID:	DOAJ048609978

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520			\|a Abstract For the lack of labels in infrared and visible image fusion network, an infrared and visible image fusion model based on multi‐channel unsupervised convolutional neural network (CNN) is proposed in this paper, in order to extract more detailed information through multi‐channel inputs. In contrast to conventional unsupervised fusion network, the proposed network contains three channels for extracting infrared features, visible features and common features of infrared and visible images, respectively. The square loss function is used to train the network. Pairs of infrared and visible images are input to DenseNet to extract as more useful features as possible. A fusion module is designed to fuse the extracted features for testing. Experimental results show that the proposed method can preserve both the clear target of infrared and detailed information of visible images simultaneously. Experiments also demonstrate the superiority of the proposed method over the state‐of‐the‐art methods in objective metrics.
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allfields	10.1049/ipr2.12431 doi (DE-627)DOAJ048609978 (DE-599)DOAJ95da2401226c4bf8b8334e388e3ed28d DE-627 ger DE-627 rakwb eng TR1-1050 QA76.75-76.765 Hongmei Wang verfasserin aut Infrared and visible image fusion based on multi‐channel convolutional neural network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract For the lack of labels in infrared and visible image fusion network, an infrared and visible image fusion model based on multi‐channel unsupervised convolutional neural network (CNN) is proposed in this paper, in order to extract more detailed information through multi‐channel inputs. In contrast to conventional unsupervised fusion network, the proposed network contains three channels for extracting infrared features, visible features and common features of infrared and visible images, respectively. The square loss function is used to train the network. Pairs of infrared and visible images are input to DenseNet to extract as more useful features as possible. A fusion module is designed to fuse the extracted features for testing. Experimental results show that the proposed method can preserve both the clear target of infrared and detailed information of visible images simultaneously. Experiments also demonstrate the superiority of the proposed method over the state‐of‐the‐art methods in objective metrics. Photography Computer software Wenbo An verfasserin aut Lin Li verfasserin aut Chenkai Li verfasserin aut Daming Zhou verfasserin aut In IET Image Processing Wiley, 2021 16(2022), 6, Seite 1575-1584 (DE-627)527265993 (DE-600)2278776-8 17519667 nnns volume:16 year:2022 number:6 pages:1575-1584 https://doi.org/10.1049/ipr2.12431 kostenfrei https://doaj.org/article/95da2401226c4bf8b8334e388e3ed28d kostenfrei https://doi.org/10.1049/ipr2.12431 kostenfrei https://doaj.org/toc/1751-9659 Journal toc kostenfrei https://doaj.org/toc/1751-9667 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 16 2022 6 1575-1584
spelling	10.1049/ipr2.12431 doi (DE-627)DOAJ048609978 (DE-599)DOAJ95da2401226c4bf8b8334e388e3ed28d DE-627 ger DE-627 rakwb eng TR1-1050 QA76.75-76.765 Hongmei Wang verfasserin aut Infrared and visible image fusion based on multi‐channel convolutional neural network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract For the lack of labels in infrared and visible image fusion network, an infrared and visible image fusion model based on multi‐channel unsupervised convolutional neural network (CNN) is proposed in this paper, in order to extract more detailed information through multi‐channel inputs. In contrast to conventional unsupervised fusion network, the proposed network contains three channels for extracting infrared features, visible features and common features of infrared and visible images, respectively. The square loss function is used to train the network. Pairs of infrared and visible images are input to DenseNet to extract as more useful features as possible. A fusion module is designed to fuse the extracted features for testing. Experimental results show that the proposed method can preserve both the clear target of infrared and detailed information of visible images simultaneously. Experiments also demonstrate the superiority of the proposed method over the state‐of‐the‐art methods in objective metrics. Photography Computer software Wenbo An verfasserin aut Lin Li verfasserin aut Chenkai Li verfasserin aut Daming Zhou verfasserin aut In IET Image Processing Wiley, 2021 16(2022), 6, Seite 1575-1584 (DE-627)527265993 (DE-600)2278776-8 17519667 nnns volume:16 year:2022 number:6 pages:1575-1584 https://doi.org/10.1049/ipr2.12431 kostenfrei https://doaj.org/article/95da2401226c4bf8b8334e388e3ed28d kostenfrei https://doi.org/10.1049/ipr2.12431 kostenfrei https://doaj.org/toc/1751-9659 Journal toc kostenfrei https://doaj.org/toc/1751-9667 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 16 2022 6 1575-1584
allfields_unstemmed	10.1049/ipr2.12431 doi (DE-627)DOAJ048609978 (DE-599)DOAJ95da2401226c4bf8b8334e388e3ed28d DE-627 ger DE-627 rakwb eng TR1-1050 QA76.75-76.765 Hongmei Wang verfasserin aut Infrared and visible image fusion based on multi‐channel convolutional neural network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract For the lack of labels in infrared and visible image fusion network, an infrared and visible image fusion model based on multi‐channel unsupervised convolutional neural network (CNN) is proposed in this paper, in order to extract more detailed information through multi‐channel inputs. In contrast to conventional unsupervised fusion network, the proposed network contains three channels for extracting infrared features, visible features and common features of infrared and visible images, respectively. The square loss function is used to train the network. Pairs of infrared and visible images are input to DenseNet to extract as more useful features as possible. A fusion module is designed to fuse the extracted features for testing. Experimental results show that the proposed method can preserve both the clear target of infrared and detailed information of visible images simultaneously. Experiments also demonstrate the superiority of the proposed method over the state‐of‐the‐art methods in objective metrics. Photography Computer software Wenbo An verfasserin aut Lin Li verfasserin aut Chenkai Li verfasserin aut Daming Zhou verfasserin aut In IET Image Processing Wiley, 2021 16(2022), 6, Seite 1575-1584 (DE-627)527265993 (DE-600)2278776-8 17519667 nnns volume:16 year:2022 number:6 pages:1575-1584 https://doi.org/10.1049/ipr2.12431 kostenfrei https://doaj.org/article/95da2401226c4bf8b8334e388e3ed28d kostenfrei https://doi.org/10.1049/ipr2.12431 kostenfrei https://doaj.org/toc/1751-9659 Journal toc kostenfrei https://doaj.org/toc/1751-9667 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 16 2022 6 1575-1584
allfieldsGer	10.1049/ipr2.12431 doi (DE-627)DOAJ048609978 (DE-599)DOAJ95da2401226c4bf8b8334e388e3ed28d DE-627 ger DE-627 rakwb eng TR1-1050 QA76.75-76.765 Hongmei Wang verfasserin aut Infrared and visible image fusion based on multi‐channel convolutional neural network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract For the lack of labels in infrared and visible image fusion network, an infrared and visible image fusion model based on multi‐channel unsupervised convolutional neural network (CNN) is proposed in this paper, in order to extract more detailed information through multi‐channel inputs. In contrast to conventional unsupervised fusion network, the proposed network contains three channels for extracting infrared features, visible features and common features of infrared and visible images, respectively. The square loss function is used to train the network. Pairs of infrared and visible images are input to DenseNet to extract as more useful features as possible. A fusion module is designed to fuse the extracted features for testing. Experimental results show that the proposed method can preserve both the clear target of infrared and detailed information of visible images simultaneously. Experiments also demonstrate the superiority of the proposed method over the state‐of‐the‐art methods in objective metrics. Photography Computer software Wenbo An verfasserin aut Lin Li verfasserin aut Chenkai Li verfasserin aut Daming Zhou verfasserin aut In IET Image Processing Wiley, 2021 16(2022), 6, Seite 1575-1584 (DE-627)527265993 (DE-600)2278776-8 17519667 nnns volume:16 year:2022 number:6 pages:1575-1584 https://doi.org/10.1049/ipr2.12431 kostenfrei https://doaj.org/article/95da2401226c4bf8b8334e388e3ed28d kostenfrei https://doi.org/10.1049/ipr2.12431 kostenfrei https://doaj.org/toc/1751-9659 Journal toc kostenfrei https://doaj.org/toc/1751-9667 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 16 2022 6 1575-1584
allfieldsSound	10.1049/ipr2.12431 doi (DE-627)DOAJ048609978 (DE-599)DOAJ95da2401226c4bf8b8334e388e3ed28d DE-627 ger DE-627 rakwb eng TR1-1050 QA76.75-76.765 Hongmei Wang verfasserin aut Infrared and visible image fusion based on multi‐channel convolutional neural network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract For the lack of labels in infrared and visible image fusion network, an infrared and visible image fusion model based on multi‐channel unsupervised convolutional neural network (CNN) is proposed in this paper, in order to extract more detailed information through multi‐channel inputs. In contrast to conventional unsupervised fusion network, the proposed network contains three channels for extracting infrared features, visible features and common features of infrared and visible images, respectively. The square loss function is used to train the network. Pairs of infrared and visible images are input to DenseNet to extract as more useful features as possible. A fusion module is designed to fuse the extracted features for testing. Experimental results show that the proposed method can preserve both the clear target of infrared and detailed information of visible images simultaneously. Experiments also demonstrate the superiority of the proposed method over the state‐of‐the‐art methods in objective metrics. Photography Computer software Wenbo An verfasserin aut Lin Li verfasserin aut Chenkai Li verfasserin aut Daming Zhou verfasserin aut In IET Image Processing Wiley, 2021 16(2022), 6, Seite 1575-1584 (DE-627)527265993 (DE-600)2278776-8 17519667 nnns volume:16 year:2022 number:6 pages:1575-1584 https://doi.org/10.1049/ipr2.12431 kostenfrei https://doaj.org/article/95da2401226c4bf8b8334e388e3ed28d kostenfrei https://doi.org/10.1049/ipr2.12431 kostenfrei https://doaj.org/toc/1751-9659 Journal toc kostenfrei https://doaj.org/toc/1751-9667 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 16 2022 6 1575-1584
language	English
source	In IET Image Processing 16(2022), 6, Seite 1575-1584 volume:16 year:2022 number:6 pages:1575-1584
sourceStr	In IET Image Processing 16(2022), 6, Seite 1575-1584 volume:16 year:2022 number:6 pages:1575-1584
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Photography Computer software
isfreeaccess_bool	true
container_title	IET Image Processing
authorswithroles_txt_mv	Hongmei Wang @@aut@@ Wenbo An @@aut@@ Lin Li @@aut@@ Chenkai Li @@aut@@ Daming Zhou @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	527265993
id	DOAJ048609978
language_de	englisch
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callnumber-first	T - Technology
author	Hongmei Wang
spellingShingle	Hongmei Wang misc TR1-1050 misc QA76.75-76.765 misc Photography misc Computer software Infrared and visible image fusion based on multi‐channel convolutional neural network
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topic_title	TR1-1050 QA76.75-76.765 Infrared and visible image fusion based on multi‐channel convolutional neural network
topic	misc TR1-1050 misc QA76.75-76.765 misc Photography misc Computer software
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title	Infrared and visible image fusion based on multi‐channel convolutional neural network
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title_full	Infrared and visible image fusion based on multi‐channel convolutional neural network
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author_browse	Hongmei Wang Wenbo An Lin Li Chenkai Li Daming Zhou
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title_sort	infrared and visible image fusion based on multi‐channel convolutional neural network
callnumber	TR1-1050
title_auth	Infrared and visible image fusion based on multi‐channel convolutional neural network
abstract	Abstract For the lack of labels in infrared and visible image fusion network, an infrared and visible image fusion model based on multi‐channel unsupervised convolutional neural network (CNN) is proposed in this paper, in order to extract more detailed information through multi‐channel inputs. In contrast to conventional unsupervised fusion network, the proposed network contains three channels for extracting infrared features, visible features and common features of infrared and visible images, respectively. The square loss function is used to train the network. Pairs of infrared and visible images are input to DenseNet to extract as more useful features as possible. A fusion module is designed to fuse the extracted features for testing. Experimental results show that the proposed method can preserve both the clear target of infrared and detailed information of visible images simultaneously. Experiments also demonstrate the superiority of the proposed method over the state‐of‐the‐art methods in objective metrics.
abstractGer	Abstract For the lack of labels in infrared and visible image fusion network, an infrared and visible image fusion model based on multi‐channel unsupervised convolutional neural network (CNN) is proposed in this paper, in order to extract more detailed information through multi‐channel inputs. In contrast to conventional unsupervised fusion network, the proposed network contains three channels for extracting infrared features, visible features and common features of infrared and visible images, respectively. The square loss function is used to train the network. Pairs of infrared and visible images are input to DenseNet to extract as more useful features as possible. A fusion module is designed to fuse the extracted features for testing. Experimental results show that the proposed method can preserve both the clear target of infrared and detailed information of visible images simultaneously. Experiments also demonstrate the superiority of the proposed method over the state‐of‐the‐art methods in objective metrics.
abstract_unstemmed	Abstract For the lack of labels in infrared and visible image fusion network, an infrared and visible image fusion model based on multi‐channel unsupervised convolutional neural network (CNN) is proposed in this paper, in order to extract more detailed information through multi‐channel inputs. In contrast to conventional unsupervised fusion network, the proposed network contains three channels for extracting infrared features, visible features and common features of infrared and visible images, respectively. The square loss function is used to train the network. Pairs of infrared and visible images are input to DenseNet to extract as more useful features as possible. A fusion module is designed to fuse the extracted features for testing. Experimental results show that the proposed method can preserve both the clear target of infrared and detailed information of visible images simultaneously. Experiments also demonstrate the superiority of the proposed method over the state‐of‐the‐art methods in objective metrics.
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title_short	Infrared and visible image fusion based on multi‐channel convolutional neural network
url	https://doi.org/10.1049/ipr2.12431 https://doaj.org/article/95da2401226c4bf8b8334e388e3ed28d https://doaj.org/toc/1751-9659 https://doaj.org/toc/1751-9667
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author2	Wenbo An Lin Li Chenkai Li Daming Zhou
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doi_str	10.1049/ipr2.12431
callnumber-a	TR1-1050
up_date	2024-07-03T18:44:22.594Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ048609978</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308135102.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1049/ipr2.12431</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ048609978</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ95da2401226c4bf8b8334e388e3ed28d</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TR1-1050</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA76.75-76.765</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Hongmei Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Infrared and visible image fusion based on multi‐channel convolutional neural network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract For the lack of labels in infrared and visible image fusion network, an infrared and visible image fusion model based on multi‐channel unsupervised convolutional neural network (CNN) is proposed in this paper, in order to extract more detailed information through multi‐channel inputs. In contrast to conventional unsupervised fusion network, the proposed network contains three channels for extracting infrared features, visible features and common features of infrared and visible images, respectively. The square loss function is used to train the network. Pairs of infrared and visible images are input to DenseNet to extract as more useful features as possible. A fusion module is designed to fuse the extracted features for testing. Experimental results show that the proposed method can preserve both the clear target of infrared and detailed information of visible images simultaneously. 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Infrared and visible image fusion based on multi‐channel convolutional neural network

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