MLF: A mimic layered fusion method for infrared and visible video

Optimal selection of fusion strategy based on image difference information is an important way to improve the adaptive performance of infrared photoelectric detection systems. Aiming at the problem that the current infrared and visible video fusion model cannot be adjusted dynamically according to t...
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Autor*in:	Guo, Xiaoming [verfasserIn] Yang, Fengbao [verfasserIn] Ji, Linna [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Multi-mimetic bionics Mimetic variant Layered fusion Infrared video Visible video

Übergeordnetes Werk:	Enthalten in: Infrared physics & technology - Amsterdam [u.a.] : Elsevier Science, 1994, 126
Übergeordnetes Werk:	volume:126

DOI / URN:	10.1016/j.infrared.2022.104349

Katalog-ID:	ELV008560366

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520			\|a Optimal selection of fusion strategy based on image difference information is an important way to improve the adaptive performance of infrared photoelectric detection systems. Aiming at the problem that the current infrared and visible video fusion model cannot be adjusted dynamically according to the difference features of video intra-frame, resulting in poor fusion effect or even fusion failure. In this paper, a mimic layered fusion method for infrared and visible video is proposed. Firstly, by comparing the general video fusion process and polymorphic process of the mimic octopus, we establish the corresponding relationship of them, and determine the four-layer variants of video mimic layered fusion. Secondly, we divide the region of interest in each frame of the video sequence, frames with significant changes in the region of interest are selected as salient frames. Thirdly, the magnitudes of various difference features of the region of interest of the salient frames are calculated respectively. Then, and a fusion effectiveness function is constructed based on cosine similarity and weighting idea to analyze and compare the fusion effects of fusion algorithms, fusion rules, fusion parameters and fusion structures on various difference features layer by layer, so as to select the optimal mimetic variant layer by layer, the mimic layered fusion is realized based on layered fusion discrimination mechanism through the optimal combination of variants. Finally, the experimental results show that our method in this paper has achieved remarkable results in preserving the typical infrared target and visible structural details in the whole video, and is significantly better than other single fusion methods in quantitative analysis and qualitative evaluation.
650		4	\|a Multi-mimetic bionics
650		4	\|a Mimetic variant
650		4	\|a Layered fusion
650		4	\|a Infrared video
650		4	\|a Visible video
700	1		\|a Yang, Fengbao \|e verfasserin \|4 aut
700	1		\|a Ji, Linna \|e verfasserin \|4 aut
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Indexfelder

author_variant	x g xg f y fy l j lj
matchkey_str	guoxiaomingyangfengbaojilinna:2022----:laiilyrduinehdoifae
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bklnumber	50.37 33.38 33.07
publishDate	2022
allfields	10.1016/j.infrared.2022.104349 doi (DE-627)ELV008560366 (ELSEVIER)S1350-4495(22)00330-9 DE-627 ger DE-627 rda eng 530 DE-600 50.37 bkl 33.38 bkl 33.07 bkl Guo, Xiaoming verfasserin aut MLF: A mimic layered fusion method for infrared and visible video 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Optimal selection of fusion strategy based on image difference information is an important way to improve the adaptive performance of infrared photoelectric detection systems. Aiming at the problem that the current infrared and visible video fusion model cannot be adjusted dynamically according to the difference features of video intra-frame, resulting in poor fusion effect or even fusion failure. In this paper, a mimic layered fusion method for infrared and visible video is proposed. Firstly, by comparing the general video fusion process and polymorphic process of the mimic octopus, we establish the corresponding relationship of them, and determine the four-layer variants of video mimic layered fusion. Secondly, we divide the region of interest in each frame of the video sequence, frames with significant changes in the region of interest are selected as salient frames. Thirdly, the magnitudes of various difference features of the region of interest of the salient frames are calculated respectively. Then, and a fusion effectiveness function is constructed based on cosine similarity and weighting idea to analyze and compare the fusion effects of fusion algorithms, fusion rules, fusion parameters and fusion structures on various difference features layer by layer, so as to select the optimal mimetic variant layer by layer, the mimic layered fusion is realized based on layered fusion discrimination mechanism through the optimal combination of variants. Finally, the experimental results show that our method in this paper has achieved remarkable results in preserving the typical infrared target and visible structural details in the whole video, and is significantly better than other single fusion methods in quantitative analysis and qualitative evaluation. Multi-mimetic bionics Mimetic variant Layered fusion Infrared video Visible video Yang, Fengbao verfasserin aut Ji, Linna verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 126 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:126 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.37 Technische Optik 33.38 Quantenoptik nichtlineare Optik 33.07 Spektroskopie AR 126
spelling	10.1016/j.infrared.2022.104349 doi (DE-627)ELV008560366 (ELSEVIER)S1350-4495(22)00330-9 DE-627 ger DE-627 rda eng 530 DE-600 50.37 bkl 33.38 bkl 33.07 bkl Guo, Xiaoming verfasserin aut MLF: A mimic layered fusion method for infrared and visible video 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Optimal selection of fusion strategy based on image difference information is an important way to improve the adaptive performance of infrared photoelectric detection systems. Aiming at the problem that the current infrared and visible video fusion model cannot be adjusted dynamically according to the difference features of video intra-frame, resulting in poor fusion effect or even fusion failure. In this paper, a mimic layered fusion method for infrared and visible video is proposed. Firstly, by comparing the general video fusion process and polymorphic process of the mimic octopus, we establish the corresponding relationship of them, and determine the four-layer variants of video mimic layered fusion. Secondly, we divide the region of interest in each frame of the video sequence, frames with significant changes in the region of interest are selected as salient frames. Thirdly, the magnitudes of various difference features of the region of interest of the salient frames are calculated respectively. Then, and a fusion effectiveness function is constructed based on cosine similarity and weighting idea to analyze and compare the fusion effects of fusion algorithms, fusion rules, fusion parameters and fusion structures on various difference features layer by layer, so as to select the optimal mimetic variant layer by layer, the mimic layered fusion is realized based on layered fusion discrimination mechanism through the optimal combination of variants. Finally, the experimental results show that our method in this paper has achieved remarkable results in preserving the typical infrared target and visible structural details in the whole video, and is significantly better than other single fusion methods in quantitative analysis and qualitative evaluation. Multi-mimetic bionics Mimetic variant Layered fusion Infrared video Visible video Yang, Fengbao verfasserin aut Ji, Linna verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 126 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:126 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.37 Technische Optik 33.38 Quantenoptik nichtlineare Optik 33.07 Spektroskopie AR 126
allfields_unstemmed	10.1016/j.infrared.2022.104349 doi (DE-627)ELV008560366 (ELSEVIER)S1350-4495(22)00330-9 DE-627 ger DE-627 rda eng 530 DE-600 50.37 bkl 33.38 bkl 33.07 bkl Guo, Xiaoming verfasserin aut MLF: A mimic layered fusion method for infrared and visible video 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Optimal selection of fusion strategy based on image difference information is an important way to improve the adaptive performance of infrared photoelectric detection systems. Aiming at the problem that the current infrared and visible video fusion model cannot be adjusted dynamically according to the difference features of video intra-frame, resulting in poor fusion effect or even fusion failure. In this paper, a mimic layered fusion method for infrared and visible video is proposed. Firstly, by comparing the general video fusion process and polymorphic process of the mimic octopus, we establish the corresponding relationship of them, and determine the four-layer variants of video mimic layered fusion. Secondly, we divide the region of interest in each frame of the video sequence, frames with significant changes in the region of interest are selected as salient frames. Thirdly, the magnitudes of various difference features of the region of interest of the salient frames are calculated respectively. Then, and a fusion effectiveness function is constructed based on cosine similarity and weighting idea to analyze and compare the fusion effects of fusion algorithms, fusion rules, fusion parameters and fusion structures on various difference features layer by layer, so as to select the optimal mimetic variant layer by layer, the mimic layered fusion is realized based on layered fusion discrimination mechanism through the optimal combination of variants. Finally, the experimental results show that our method in this paper has achieved remarkable results in preserving the typical infrared target and visible structural details in the whole video, and is significantly better than other single fusion methods in quantitative analysis and qualitative evaluation. Multi-mimetic bionics Mimetic variant Layered fusion Infrared video Visible video Yang, Fengbao verfasserin aut Ji, Linna verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 126 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:126 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.37 Technische Optik 33.38 Quantenoptik nichtlineare Optik 33.07 Spektroskopie AR 126
allfieldsGer	10.1016/j.infrared.2022.104349 doi (DE-627)ELV008560366 (ELSEVIER)S1350-4495(22)00330-9 DE-627 ger DE-627 rda eng 530 DE-600 50.37 bkl 33.38 bkl 33.07 bkl Guo, Xiaoming verfasserin aut MLF: A mimic layered fusion method for infrared and visible video 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Optimal selection of fusion strategy based on image difference information is an important way to improve the adaptive performance of infrared photoelectric detection systems. Aiming at the problem that the current infrared and visible video fusion model cannot be adjusted dynamically according to the difference features of video intra-frame, resulting in poor fusion effect or even fusion failure. In this paper, a mimic layered fusion method for infrared and visible video is proposed. Firstly, by comparing the general video fusion process and polymorphic process of the mimic octopus, we establish the corresponding relationship of them, and determine the four-layer variants of video mimic layered fusion. Secondly, we divide the region of interest in each frame of the video sequence, frames with significant changes in the region of interest are selected as salient frames. Thirdly, the magnitudes of various difference features of the region of interest of the salient frames are calculated respectively. Then, and a fusion effectiveness function is constructed based on cosine similarity and weighting idea to analyze and compare the fusion effects of fusion algorithms, fusion rules, fusion parameters and fusion structures on various difference features layer by layer, so as to select the optimal mimetic variant layer by layer, the mimic layered fusion is realized based on layered fusion discrimination mechanism through the optimal combination of variants. Finally, the experimental results show that our method in this paper has achieved remarkable results in preserving the typical infrared target and visible structural details in the whole video, and is significantly better than other single fusion methods in quantitative analysis and qualitative evaluation. Multi-mimetic bionics Mimetic variant Layered fusion Infrared video Visible video Yang, Fengbao verfasserin aut Ji, Linna verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 126 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:126 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.37 Technische Optik 33.38 Quantenoptik nichtlineare Optik 33.07 Spektroskopie AR 126
allfieldsSound	10.1016/j.infrared.2022.104349 doi (DE-627)ELV008560366 (ELSEVIER)S1350-4495(22)00330-9 DE-627 ger DE-627 rda eng 530 DE-600 50.37 bkl 33.38 bkl 33.07 bkl Guo, Xiaoming verfasserin aut MLF: A mimic layered fusion method for infrared and visible video 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Optimal selection of fusion strategy based on image difference information is an important way to improve the adaptive performance of infrared photoelectric detection systems. Aiming at the problem that the current infrared and visible video fusion model cannot be adjusted dynamically according to the difference features of video intra-frame, resulting in poor fusion effect or even fusion failure. In this paper, a mimic layered fusion method for infrared and visible video is proposed. Firstly, by comparing the general video fusion process and polymorphic process of the mimic octopus, we establish the corresponding relationship of them, and determine the four-layer variants of video mimic layered fusion. Secondly, we divide the region of interest in each frame of the video sequence, frames with significant changes in the region of interest are selected as salient frames. Thirdly, the magnitudes of various difference features of the region of interest of the salient frames are calculated respectively. Then, and a fusion effectiveness function is constructed based on cosine similarity and weighting idea to analyze and compare the fusion effects of fusion algorithms, fusion rules, fusion parameters and fusion structures on various difference features layer by layer, so as to select the optimal mimetic variant layer by layer, the mimic layered fusion is realized based on layered fusion discrimination mechanism through the optimal combination of variants. Finally, the experimental results show that our method in this paper has achieved remarkable results in preserving the typical infrared target and visible structural details in the whole video, and is significantly better than other single fusion methods in quantitative analysis and qualitative evaluation. Multi-mimetic bionics Mimetic variant Layered fusion Infrared video Visible video Yang, Fengbao verfasserin aut Ji, Linna verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 126 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:126 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.37 Technische Optik 33.38 Quantenoptik nichtlineare Optik 33.07 Spektroskopie AR 126
language	English
source	Enthalten in Infrared physics & technology 126 volume:126
sourceStr	Enthalten in Infrared physics & technology 126 volume:126
format_phy_str_mv	Article
bklname	Technische Optik Quantenoptik nichtlineare Optik Spektroskopie
institution	findex.gbv.de
topic_facet	Multi-mimetic bionics Mimetic variant Layered fusion Infrared video Visible video
dewey-raw	530
isfreeaccess_bool	false
container_title	Infrared physics & technology
authorswithroles_txt_mv	Guo, Xiaoming @@aut@@ Yang, Fengbao @@aut@@ Ji, Linna @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	320592146
dewey-sort	3530
id	ELV008560366
language_de	englisch
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author	Guo, Xiaoming
spellingShingle	Guo, Xiaoming ddc 530 bkl 50.37 bkl 33.38 bkl 33.07 misc Multi-mimetic bionics misc Mimetic variant misc Layered fusion misc Infrared video misc Visible video MLF: A mimic layered fusion method for infrared and visible video
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topic_title	530 DE-600 50.37 bkl 33.38 bkl 33.07 bkl MLF: A mimic layered fusion method for infrared and visible video Multi-mimetic bionics Mimetic variant Layered fusion Infrared video Visible video
topic	ddc 530 bkl 50.37 bkl 33.38 bkl 33.07 misc Multi-mimetic bionics misc Mimetic variant misc Layered fusion misc Infrared video misc Visible video
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title	MLF: A mimic layered fusion method for infrared and visible video
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title_full	MLF: A mimic layered fusion method for infrared and visible video
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title_sort	mlf: a mimic layered fusion method for infrared and visible video
title_auth	MLF: A mimic layered fusion method for infrared and visible video
abstract	Optimal selection of fusion strategy based on image difference information is an important way to improve the adaptive performance of infrared photoelectric detection systems. Aiming at the problem that the current infrared and visible video fusion model cannot be adjusted dynamically according to the difference features of video intra-frame, resulting in poor fusion effect or even fusion failure. In this paper, a mimic layered fusion method for infrared and visible video is proposed. Firstly, by comparing the general video fusion process and polymorphic process of the mimic octopus, we establish the corresponding relationship of them, and determine the four-layer variants of video mimic layered fusion. Secondly, we divide the region of interest in each frame of the video sequence, frames with significant changes in the region of interest are selected as salient frames. Thirdly, the magnitudes of various difference features of the region of interest of the salient frames are calculated respectively. Then, and a fusion effectiveness function is constructed based on cosine similarity and weighting idea to analyze and compare the fusion effects of fusion algorithms, fusion rules, fusion parameters and fusion structures on various difference features layer by layer, so as to select the optimal mimetic variant layer by layer, the mimic layered fusion is realized based on layered fusion discrimination mechanism through the optimal combination of variants. Finally, the experimental results show that our method in this paper has achieved remarkable results in preserving the typical infrared target and visible structural details in the whole video, and is significantly better than other single fusion methods in quantitative analysis and qualitative evaluation.
abstractGer	Optimal selection of fusion strategy based on image difference information is an important way to improve the adaptive performance of infrared photoelectric detection systems. Aiming at the problem that the current infrared and visible video fusion model cannot be adjusted dynamically according to the difference features of video intra-frame, resulting in poor fusion effect or even fusion failure. In this paper, a mimic layered fusion method for infrared and visible video is proposed. Firstly, by comparing the general video fusion process and polymorphic process of the mimic octopus, we establish the corresponding relationship of them, and determine the four-layer variants of video mimic layered fusion. Secondly, we divide the region of interest in each frame of the video sequence, frames with significant changes in the region of interest are selected as salient frames. Thirdly, the magnitudes of various difference features of the region of interest of the salient frames are calculated respectively. Then, and a fusion effectiveness function is constructed based on cosine similarity and weighting idea to analyze and compare the fusion effects of fusion algorithms, fusion rules, fusion parameters and fusion structures on various difference features layer by layer, so as to select the optimal mimetic variant layer by layer, the mimic layered fusion is realized based on layered fusion discrimination mechanism through the optimal combination of variants. Finally, the experimental results show that our method in this paper has achieved remarkable results in preserving the typical infrared target and visible structural details in the whole video, and is significantly better than other single fusion methods in quantitative analysis and qualitative evaluation.
abstract_unstemmed	Optimal selection of fusion strategy based on image difference information is an important way to improve the adaptive performance of infrared photoelectric detection systems. Aiming at the problem that the current infrared and visible video fusion model cannot be adjusted dynamically according to the difference features of video intra-frame, resulting in poor fusion effect or even fusion failure. In this paper, a mimic layered fusion method for infrared and visible video is proposed. Firstly, by comparing the general video fusion process and polymorphic process of the mimic octopus, we establish the corresponding relationship of them, and determine the four-layer variants of video mimic layered fusion. Secondly, we divide the region of interest in each frame of the video sequence, frames with significant changes in the region of interest are selected as salient frames. Thirdly, the magnitudes of various difference features of the region of interest of the salient frames are calculated respectively. Then, and a fusion effectiveness function is constructed based on cosine similarity and weighting idea to analyze and compare the fusion effects of fusion algorithms, fusion rules, fusion parameters and fusion structures on various difference features layer by layer, so as to select the optimal mimetic variant layer by layer, the mimic layered fusion is realized based on layered fusion discrimination mechanism through the optimal combination of variants. Finally, the experimental results show that our method in this paper has achieved remarkable results in preserving the typical infrared target and visible structural details in the whole video, and is significantly better than other single fusion methods in quantitative analysis and qualitative evaluation.
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title_short	MLF: A mimic layered fusion method for infrared and visible video
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doi_str	10.1016/j.infrared.2022.104349
up_date	2024-07-06T20:07:11.663Z
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