Computational ghost imaging based on an untrained neural network

Ghost imaging based on deep learning (DLGI) usually employs a supervised learning strategy, and needs a large set of labeled data to train a neural network. However, in many practical applications, it is difficult to obtain sufficient numbers of labeled data for training and the training process oft...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Liu, Shoupei [verfasserIn] Meng, Xiangfeng [verfasserIn] Yin, Yongkai [verfasserIn] Wu, Huazheng [verfasserIn] Jiang, Wenjie [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Computational ghost imaging Untrained neural network Deep learning

Übergeordnetes Werk:	Enthalten in: Optics and lasers in engineering - Amsterdam [u.a.] : Elsevier Science, 1980, 147
Übergeordnetes Werk:	volume:147

DOI / URN:	10.1016/j.optlaseng.2021.106744

Katalog-ID:	ELV006397034

Internformat


LEADER	01000caa a22002652 4500
001	ELV006397034
003	DE-627
005	20230524125641.0
007	cr uuu---uuuuu
008	230505s2021 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.optlaseng.2021.106744 \|2 doi
035			\|a (DE-627)ELV006397034
035			\|a (ELSEVIER)S0143-8166(21)00214-1
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
082	0	4	\|a 530 \|q DE-600
084			\|a 50.37 \|2 bkl
100	1		\|a Liu, Shoupei \|e verfasserin \|4 aut
245	1	0	\|a Computational ghost imaging based on an untrained neural network
264		1	\|c 2021
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Ghost imaging based on deep learning (DLGI) usually employs a supervised learning strategy, and needs a large set of labeled data to train a neural network. However, in many practical applications, it is difficult to obtain sufficient numbers of labeled data for training and the training process often takes a long time. Thus, a computational ghost imaging method based on deep learning using an untrained neural network (UNNCGI) is proposed. The input to the network is just a set of one-dimensional light intensity values collected by a single-pixel detector and the neural network can be automatically optimized to generate restored images through the interaction between the network and the process of computational ghost imaging. Both simulation and experiment confirm the feasibility of this untrained network. The reconstructed image of UNNCGI has good quality, even at low sampling ratios, which improves the imaging efficiency and will promote the practical applications of ghost imaging.
650		4	\|a Computational ghost imaging
650		4	\|a Untrained neural network
650		4	\|a Deep learning
700	1		\|a Meng, Xiangfeng \|e verfasserin \|4 aut
700	1		\|a Yin, Yongkai \|e verfasserin \|4 aut
700	1		\|a Wu, Huazheng \|e verfasserin \|4 aut
700	1		\|a Jiang, Wenjie \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Optics and lasers in engineering \|d Amsterdam [u.a.] : Elsevier Science, 1980 \|g 147 \|h Online-Ressource \|w (DE-627)320405737 \|w (DE-600)2000651-2 \|w (DE-576)259484369 \|x 0143-8166 \|7 nnns
773	1	8	\|g volume:147
912			\|a GBV_USEFLAG_U
912			\|a SYSFLAG_U
912			\|a GBV_ELV
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 50.37 \|j Technische Optik
951			\|a AR
952			\|d 147

Indexfelder

author_variant	s l sl x m xm y y yy h w hw w j wj
matchkey_str	article:01438166:2021----::opttoagotmgnbsdnnnrie
hierarchy_sort_str	2021
bklnumber	50.37
publishDate	2021
allfields	10.1016/j.optlaseng.2021.106744 doi (DE-627)ELV006397034 (ELSEVIER)S0143-8166(21)00214-1 DE-627 ger DE-627 rda eng 530 DE-600 50.37 bkl Liu, Shoupei verfasserin aut Computational ghost imaging based on an untrained neural network 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ghost imaging based on deep learning (DLGI) usually employs a supervised learning strategy, and needs a large set of labeled data to train a neural network. However, in many practical applications, it is difficult to obtain sufficient numbers of labeled data for training and the training process often takes a long time. Thus, a computational ghost imaging method based on deep learning using an untrained neural network (UNNCGI) is proposed. The input to the network is just a set of one-dimensional light intensity values collected by a single-pixel detector and the neural network can be automatically optimized to generate restored images through the interaction between the network and the process of computational ghost imaging. Both simulation and experiment confirm the feasibility of this untrained network. The reconstructed image of UNNCGI has good quality, even at low sampling ratios, which improves the imaging efficiency and will promote the practical applications of ghost imaging. Computational ghost imaging Untrained neural network Deep learning Meng, Xiangfeng verfasserin aut Yin, Yongkai verfasserin aut Wu, Huazheng verfasserin aut Jiang, Wenjie verfasserin aut Enthalten in Optics and lasers in engineering Amsterdam [u.a.] : Elsevier Science, 1980 147 Online-Ressource (DE-627)320405737 (DE-600)2000651-2 (DE-576)259484369 0143-8166 nnns volume:147 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.37 Technische Optik AR 147
spelling	10.1016/j.optlaseng.2021.106744 doi (DE-627)ELV006397034 (ELSEVIER)S0143-8166(21)00214-1 DE-627 ger DE-627 rda eng 530 DE-600 50.37 bkl Liu, Shoupei verfasserin aut Computational ghost imaging based on an untrained neural network 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ghost imaging based on deep learning (DLGI) usually employs a supervised learning strategy, and needs a large set of labeled data to train a neural network. However, in many practical applications, it is difficult to obtain sufficient numbers of labeled data for training and the training process often takes a long time. Thus, a computational ghost imaging method based on deep learning using an untrained neural network (UNNCGI) is proposed. The input to the network is just a set of one-dimensional light intensity values collected by a single-pixel detector and the neural network can be automatically optimized to generate restored images through the interaction between the network and the process of computational ghost imaging. Both simulation and experiment confirm the feasibility of this untrained network. The reconstructed image of UNNCGI has good quality, even at low sampling ratios, which improves the imaging efficiency and will promote the practical applications of ghost imaging. Computational ghost imaging Untrained neural network Deep learning Meng, Xiangfeng verfasserin aut Yin, Yongkai verfasserin aut Wu, Huazheng verfasserin aut Jiang, Wenjie verfasserin aut Enthalten in Optics and lasers in engineering Amsterdam [u.a.] : Elsevier Science, 1980 147 Online-Ressource (DE-627)320405737 (DE-600)2000651-2 (DE-576)259484369 0143-8166 nnns volume:147 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.37 Technische Optik AR 147
allfields_unstemmed	10.1016/j.optlaseng.2021.106744 doi (DE-627)ELV006397034 (ELSEVIER)S0143-8166(21)00214-1 DE-627 ger DE-627 rda eng 530 DE-600 50.37 bkl Liu, Shoupei verfasserin aut Computational ghost imaging based on an untrained neural network 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ghost imaging based on deep learning (DLGI) usually employs a supervised learning strategy, and needs a large set of labeled data to train a neural network. However, in many practical applications, it is difficult to obtain sufficient numbers of labeled data for training and the training process often takes a long time. Thus, a computational ghost imaging method based on deep learning using an untrained neural network (UNNCGI) is proposed. The input to the network is just a set of one-dimensional light intensity values collected by a single-pixel detector and the neural network can be automatically optimized to generate restored images through the interaction between the network and the process of computational ghost imaging. Both simulation and experiment confirm the feasibility of this untrained network. The reconstructed image of UNNCGI has good quality, even at low sampling ratios, which improves the imaging efficiency and will promote the practical applications of ghost imaging. Computational ghost imaging Untrained neural network Deep learning Meng, Xiangfeng verfasserin aut Yin, Yongkai verfasserin aut Wu, Huazheng verfasserin aut Jiang, Wenjie verfasserin aut Enthalten in Optics and lasers in engineering Amsterdam [u.a.] : Elsevier Science, 1980 147 Online-Ressource (DE-627)320405737 (DE-600)2000651-2 (DE-576)259484369 0143-8166 nnns volume:147 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.37 Technische Optik AR 147
allfieldsGer	10.1016/j.optlaseng.2021.106744 doi (DE-627)ELV006397034 (ELSEVIER)S0143-8166(21)00214-1 DE-627 ger DE-627 rda eng 530 DE-600 50.37 bkl Liu, Shoupei verfasserin aut Computational ghost imaging based on an untrained neural network 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ghost imaging based on deep learning (DLGI) usually employs a supervised learning strategy, and needs a large set of labeled data to train a neural network. However, in many practical applications, it is difficult to obtain sufficient numbers of labeled data for training and the training process often takes a long time. Thus, a computational ghost imaging method based on deep learning using an untrained neural network (UNNCGI) is proposed. The input to the network is just a set of one-dimensional light intensity values collected by a single-pixel detector and the neural network can be automatically optimized to generate restored images through the interaction between the network and the process of computational ghost imaging. Both simulation and experiment confirm the feasibility of this untrained network. The reconstructed image of UNNCGI has good quality, even at low sampling ratios, which improves the imaging efficiency and will promote the practical applications of ghost imaging. Computational ghost imaging Untrained neural network Deep learning Meng, Xiangfeng verfasserin aut Yin, Yongkai verfasserin aut Wu, Huazheng verfasserin aut Jiang, Wenjie verfasserin aut Enthalten in Optics and lasers in engineering Amsterdam [u.a.] : Elsevier Science, 1980 147 Online-Ressource (DE-627)320405737 (DE-600)2000651-2 (DE-576)259484369 0143-8166 nnns volume:147 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.37 Technische Optik AR 147
allfieldsSound	10.1016/j.optlaseng.2021.106744 doi (DE-627)ELV006397034 (ELSEVIER)S0143-8166(21)00214-1 DE-627 ger DE-627 rda eng 530 DE-600 50.37 bkl Liu, Shoupei verfasserin aut Computational ghost imaging based on an untrained neural network 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ghost imaging based on deep learning (DLGI) usually employs a supervised learning strategy, and needs a large set of labeled data to train a neural network. However, in many practical applications, it is difficult to obtain sufficient numbers of labeled data for training and the training process often takes a long time. Thus, a computational ghost imaging method based on deep learning using an untrained neural network (UNNCGI) is proposed. The input to the network is just a set of one-dimensional light intensity values collected by a single-pixel detector and the neural network can be automatically optimized to generate restored images through the interaction between the network and the process of computational ghost imaging. Both simulation and experiment confirm the feasibility of this untrained network. The reconstructed image of UNNCGI has good quality, even at low sampling ratios, which improves the imaging efficiency and will promote the practical applications of ghost imaging. Computational ghost imaging Untrained neural network Deep learning Meng, Xiangfeng verfasserin aut Yin, Yongkai verfasserin aut Wu, Huazheng verfasserin aut Jiang, Wenjie verfasserin aut Enthalten in Optics and lasers in engineering Amsterdam [u.a.] : Elsevier Science, 1980 147 Online-Ressource (DE-627)320405737 (DE-600)2000651-2 (DE-576)259484369 0143-8166 nnns volume:147 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.37 Technische Optik AR 147
language	English
source	Enthalten in Optics and lasers in engineering 147 volume:147
sourceStr	Enthalten in Optics and lasers in engineering 147 volume:147
format_phy_str_mv	Article
bklname	Technische Optik
institution	findex.gbv.de
topic_facet	Computational ghost imaging Untrained neural network Deep learning
dewey-raw	530
isfreeaccess_bool	false
container_title	Optics and lasers in engineering
authorswithroles_txt_mv	Liu, Shoupei @@aut@@ Meng, Xiangfeng @@aut@@ Yin, Yongkai @@aut@@ Wu, Huazheng @@aut@@ Jiang, Wenjie @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	320405737
dewey-sort	3530
id	ELV006397034
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV006397034</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524125641.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230505s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.optlaseng.2021.106744</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV006397034</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0143-8166(21)00214-1</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">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">530</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.37</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Liu, Shoupei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Computational ghost imaging based on an untrained neural network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</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">Ghost imaging based on deep learning (DLGI) usually employs a supervised learning strategy, and needs a large set of labeled data to train a neural network. However, in many practical applications, it is difficult to obtain sufficient numbers of labeled data for training and the training process often takes a long time. Thus, a computational ghost imaging method based on deep learning using an untrained neural network (UNNCGI) is proposed. The input to the network is just a set of one-dimensional light intensity values collected by a single-pixel detector and the neural network can be automatically optimized to generate restored images through the interaction between the network and the process of computational ghost imaging. Both simulation and experiment confirm the feasibility of this untrained network. The reconstructed image of UNNCGI has good quality, even at low sampling ratios, which improves the imaging efficiency and will promote the practical applications of ghost imaging.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computational ghost imaging</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Untrained neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Meng, Xiangfeng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yin, Yongkai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Huazheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jiang, Wenjie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Optics and lasers in engineering</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1980</subfield><subfield code="g">147</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)320405737</subfield><subfield code="w">(DE-600)2000651-2</subfield><subfield code="w">(DE-576)259484369</subfield><subfield code="x">0143-8166</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.37</subfield><subfield code="j">Technische Optik</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">147</subfield></datafield></record></collection>
author	Liu, Shoupei
spellingShingle	Liu, Shoupei ddc 530 bkl 50.37 misc Computational ghost imaging misc Untrained neural network misc Deep learning Computational ghost imaging based on an untrained neural network
authorStr	Liu, Shoupei
ppnlink_with_tag_str_mv	@@773@@(DE-627)320405737
format	electronic Article
dewey-ones	530 - Physics
delete_txt_mv	keep
author_role	aut aut aut aut aut
collection	elsevier
remote_str	true
illustrated	Not Illustrated
issn	0143-8166
topic_title	530 DE-600 50.37 bkl Computational ghost imaging based on an untrained neural network Computational ghost imaging Untrained neural network Deep learning
topic	ddc 530 bkl 50.37 misc Computational ghost imaging misc Untrained neural network misc Deep learning
topic_unstemmed	ddc 530 bkl 50.37 misc Computational ghost imaging misc Untrained neural network misc Deep learning
topic_browse	ddc 530 bkl 50.37 misc Computational ghost imaging misc Untrained neural network misc Deep learning
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Optics and lasers in engineering
hierarchy_parent_id	320405737
dewey-tens	530 - Physics
hierarchy_top_title	Optics and lasers in engineering
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)320405737 (DE-600)2000651-2 (DE-576)259484369
title	Computational ghost imaging based on an untrained neural network
ctrlnum	(DE-627)ELV006397034 (ELSEVIER)S0143-8166(21)00214-1
title_full	Computational ghost imaging based on an untrained neural network
author_sort	Liu, Shoupei
journal	Optics and lasers in engineering
journalStr	Optics and lasers in engineering
lang_code	eng
isOA_bool	false
dewey-hundreds	500 - Science
recordtype	marc
publishDateSort	2021
contenttype_str_mv	zzz
author_browse	Liu, Shoupei Meng, Xiangfeng Yin, Yongkai Wu, Huazheng Jiang, Wenjie
container_volume	147
class	530 DE-600 50.37 bkl
format_se	Elektronische Aufsätze
author-letter	Liu, Shoupei
doi_str_mv	10.1016/j.optlaseng.2021.106744
dewey-full	530
author2-role	verfasserin
title_sort	computational ghost imaging based on an untrained neural network
title_auth	Computational ghost imaging based on an untrained neural network
abstract	Ghost imaging based on deep learning (DLGI) usually employs a supervised learning strategy, and needs a large set of labeled data to train a neural network. However, in many practical applications, it is difficult to obtain sufficient numbers of labeled data for training and the training process often takes a long time. Thus, a computational ghost imaging method based on deep learning using an untrained neural network (UNNCGI) is proposed. The input to the network is just a set of one-dimensional light intensity values collected by a single-pixel detector and the neural network can be automatically optimized to generate restored images through the interaction between the network and the process of computational ghost imaging. Both simulation and experiment confirm the feasibility of this untrained network. The reconstructed image of UNNCGI has good quality, even at low sampling ratios, which improves the imaging efficiency and will promote the practical applications of ghost imaging.
abstractGer	Ghost imaging based on deep learning (DLGI) usually employs a supervised learning strategy, and needs a large set of labeled data to train a neural network. However, in many practical applications, it is difficult to obtain sufficient numbers of labeled data for training and the training process often takes a long time. Thus, a computational ghost imaging method based on deep learning using an untrained neural network (UNNCGI) is proposed. The input to the network is just a set of one-dimensional light intensity values collected by a single-pixel detector and the neural network can be automatically optimized to generate restored images through the interaction between the network and the process of computational ghost imaging. Both simulation and experiment confirm the feasibility of this untrained network. The reconstructed image of UNNCGI has good quality, even at low sampling ratios, which improves the imaging efficiency and will promote the practical applications of ghost imaging.
abstract_unstemmed	Ghost imaging based on deep learning (DLGI) usually employs a supervised learning strategy, and needs a large set of labeled data to train a neural network. However, in many practical applications, it is difficult to obtain sufficient numbers of labeled data for training and the training process often takes a long time. Thus, a computational ghost imaging method based on deep learning using an untrained neural network (UNNCGI) is proposed. The input to the network is just a set of one-dimensional light intensity values collected by a single-pixel detector and the neural network can be automatically optimized to generate restored images through the interaction between the network and the process of computational ghost imaging. Both simulation and experiment confirm the feasibility of this untrained network. The reconstructed image of UNNCGI has good quality, even at low sampling ratios, which improves the imaging efficiency and will promote the practical applications of ghost imaging.
collection_details	GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393
title_short	Computational ghost imaging based on an untrained neural network
remote_bool	true
author2	Meng, Xiangfeng Yin, Yongkai Wu, Huazheng Jiang, Wenjie
author2Str	Meng, Xiangfeng Yin, Yongkai Wu, Huazheng Jiang, Wenjie
ppnlink	320405737
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1016/j.optlaseng.2021.106744
up_date	2024-07-06T21:14:28.845Z
_version_	1803865784421711872
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">ELV006397034</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524125641.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230505s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.optlaseng.2021.106744</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV006397034</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0143-8166(21)00214-1</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">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">530</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.37</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Liu, Shoupei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Computational ghost imaging based on an untrained neural network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</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">Ghost imaging based on deep learning (DLGI) usually employs a supervised learning strategy, and needs a large set of labeled data to train a neural network. However, in many practical applications, it is difficult to obtain sufficient numbers of labeled data for training and the training process often takes a long time. Thus, a computational ghost imaging method based on deep learning using an untrained neural network (UNNCGI) is proposed. The input to the network is just a set of one-dimensional light intensity values collected by a single-pixel detector and the neural network can be automatically optimized to generate restored images through the interaction between the network and the process of computational ghost imaging. Both simulation and experiment confirm the feasibility of this untrained network. The reconstructed image of UNNCGI has good quality, even at low sampling ratios, which improves the imaging efficiency and will promote the practical applications of ghost imaging.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computational ghost imaging</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Untrained neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Meng, Xiangfeng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yin, Yongkai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Huazheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jiang, Wenjie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Optics and lasers in engineering</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1980</subfield><subfield code="g">147</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)320405737</subfield><subfield code="w">(DE-600)2000651-2</subfield><subfield code="w">(DE-576)259484369</subfield><subfield code="x">0143-8166</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.37</subfield><subfield code="j">Technische Optik</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">147</subfield></datafield></record></collection>
score	7.402766

Nicht das Richtige dabei?

Schreiben Sie uns!

Computational ghost imaging based on an untrained neural network

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?