Single shot real-time high-resolution imaging through dynamic turbid media based on deep learning
• We scrutinize the Fourier-domain shower-curtain effect (FDSE)-related noise model, which makes it possible to simulate adequate training data to learn FDSE correlography problems. • Without knowing the experimental scene, the generated convolutional neural network (CNN) is computationally efficien...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wu, Huazheng [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
|---|
| Übergeordnetes Werk: |
Enthalten in: Performance enhancement strategies of a hybrid solar chimney power plant integrated with photovoltaic panel - Pratap Singh, Ajeet ELSEVIER, 2020, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:149 ; year:2022 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.optlaseng.2021.106819 |
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ELV055709524 |
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| 520 | |a • We scrutinize the Fourier-domain shower-curtain effect (FDSE)-related noise model, which makes it possible to simulate adequate training data to learn FDSE correlography problems. • Without knowing the experimental scene, the generated convolutional neural network (CNN) is computationally efficient and extremely robust to various forms of noise, far exceeding the capabilities of existing algorithms. • The targets can be reconstructed from a standard sCMOS detector with a 150 ms exposure. Optical experiments have proved that in dynamic turbid media, the real-time high-resolution imaging with a single lens is possible. | ||
| 520 | |a • We scrutinize the Fourier-domain shower-curtain effect (FDSE)-related noise model, which makes it possible to simulate adequate training data to learn FDSE correlography problems. • Without knowing the experimental scene, the generated convolutional neural network (CNN) is computationally efficient and extremely robust to various forms of noise, far exceeding the capabilities of existing algorithms. • The targets can be reconstructed from a standard sCMOS detector with a 150 ms exposure. Optical experiments have proved that in dynamic turbid media, the real-time high-resolution imaging with a single lens is possible. | ||
| 700 | 1 | |a Meng, Xiangfeng |4 oth | |
| 700 | 1 | |a Yang, Xiulun |4 oth | |
| 700 | 1 | |a Li, Xianye |4 oth | |
| 700 | 1 | |a Yin, Yongkai |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Pratap Singh, Ajeet ELSEVIER |t Performance enhancement strategies of a hybrid solar chimney power plant integrated with photovoltaic panel |d 2020 |g Amsterdam [u.a.] |w (DE-627)ELV004269535 |
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10.1016/j.optlaseng.2021.106819 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001565.pica (DE-627)ELV055709524 (ELSEVIER)S0143-8166(21)00289-X DE-627 ger DE-627 rakwb eng 620 VZ 50.70 bkl 83.65 bkl 52.57 bkl 52.56 bkl Wu, Huazheng verfasserin aut Single shot real-time high-resolution imaging through dynamic turbid media based on deep learning 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • We scrutinize the Fourier-domain shower-curtain effect (FDSE)-related noise model, which makes it possible to simulate adequate training data to learn FDSE correlography problems. • Without knowing the experimental scene, the generated convolutional neural network (CNN) is computationally efficient and extremely robust to various forms of noise, far exceeding the capabilities of existing algorithms. • The targets can be reconstructed from a standard sCMOS detector with a 150 ms exposure. Optical experiments have proved that in dynamic turbid media, the real-time high-resolution imaging with a single lens is possible. • We scrutinize the Fourier-domain shower-curtain effect (FDSE)-related noise model, which makes it possible to simulate adequate training data to learn FDSE correlography problems. • Without knowing the experimental scene, the generated convolutional neural network (CNN) is computationally efficient and extremely robust to various forms of noise, far exceeding the capabilities of existing algorithms. • The targets can be reconstructed from a standard sCMOS detector with a 150 ms exposure. Optical experiments have proved that in dynamic turbid media, the real-time high-resolution imaging with a single lens is possible. Meng, Xiangfeng oth Yang, Xiulun oth Li, Xianye oth Yin, Yongkai oth Enthalten in Elsevier Science Pratap Singh, Ajeet ELSEVIER Performance enhancement strategies of a hybrid solar chimney power plant integrated with photovoltaic panel 2020 Amsterdam [u.a.] (DE-627)ELV004269535 volume:149 year:2022 pages:0 https://doi.org/10.1016/j.optlaseng.2021.106819 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 50.70 Energie: Allgemeines VZ 83.65 Versorgungswirtschaft VZ 52.57 Energiespeicherung VZ 52.56 Regenerative Energieformen alternative Energieformen VZ AR 149 2022 0 |
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10.1016/j.optlaseng.2021.106819 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001565.pica (DE-627)ELV055709524 (ELSEVIER)S0143-8166(21)00289-X DE-627 ger DE-627 rakwb eng 620 VZ 50.70 bkl 83.65 bkl 52.57 bkl 52.56 bkl Wu, Huazheng verfasserin aut Single shot real-time high-resolution imaging through dynamic turbid media based on deep learning 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • We scrutinize the Fourier-domain shower-curtain effect (FDSE)-related noise model, which makes it possible to simulate adequate training data to learn FDSE correlography problems. • Without knowing the experimental scene, the generated convolutional neural network (CNN) is computationally efficient and extremely robust to various forms of noise, far exceeding the capabilities of existing algorithms. • The targets can be reconstructed from a standard sCMOS detector with a 150 ms exposure. Optical experiments have proved that in dynamic turbid media, the real-time high-resolution imaging with a single lens is possible. • We scrutinize the Fourier-domain shower-curtain effect (FDSE)-related noise model, which makes it possible to simulate adequate training data to learn FDSE correlography problems. • Without knowing the experimental scene, the generated convolutional neural network (CNN) is computationally efficient and extremely robust to various forms of noise, far exceeding the capabilities of existing algorithms. • The targets can be reconstructed from a standard sCMOS detector with a 150 ms exposure. Optical experiments have proved that in dynamic turbid media, the real-time high-resolution imaging with a single lens is possible. Meng, Xiangfeng oth Yang, Xiulun oth Li, Xianye oth Yin, Yongkai oth Enthalten in Elsevier Science Pratap Singh, Ajeet ELSEVIER Performance enhancement strategies of a hybrid solar chimney power plant integrated with photovoltaic panel 2020 Amsterdam [u.a.] (DE-627)ELV004269535 volume:149 year:2022 pages:0 https://doi.org/10.1016/j.optlaseng.2021.106819 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 50.70 Energie: Allgemeines VZ 83.65 Versorgungswirtschaft VZ 52.57 Energiespeicherung VZ 52.56 Regenerative Energieformen alternative Energieformen VZ AR 149 2022 0 |
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10.1016/j.optlaseng.2021.106819 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001565.pica (DE-627)ELV055709524 (ELSEVIER)S0143-8166(21)00289-X DE-627 ger DE-627 rakwb eng 620 VZ 50.70 bkl 83.65 bkl 52.57 bkl 52.56 bkl Wu, Huazheng verfasserin aut Single shot real-time high-resolution imaging through dynamic turbid media based on deep learning 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • We scrutinize the Fourier-domain shower-curtain effect (FDSE)-related noise model, which makes it possible to simulate adequate training data to learn FDSE correlography problems. • Without knowing the experimental scene, the generated convolutional neural network (CNN) is computationally efficient and extremely robust to various forms of noise, far exceeding the capabilities of existing algorithms. • The targets can be reconstructed from a standard sCMOS detector with a 150 ms exposure. Optical experiments have proved that in dynamic turbid media, the real-time high-resolution imaging with a single lens is possible. • We scrutinize the Fourier-domain shower-curtain effect (FDSE)-related noise model, which makes it possible to simulate adequate training data to learn FDSE correlography problems. • Without knowing the experimental scene, the generated convolutional neural network (CNN) is computationally efficient and extremely robust to various forms of noise, far exceeding the capabilities of existing algorithms. • The targets can be reconstructed from a standard sCMOS detector with a 150 ms exposure. Optical experiments have proved that in dynamic turbid media, the real-time high-resolution imaging with a single lens is possible. Meng, Xiangfeng oth Yang, Xiulun oth Li, Xianye oth Yin, Yongkai oth Enthalten in Elsevier Science Pratap Singh, Ajeet ELSEVIER Performance enhancement strategies of a hybrid solar chimney power plant integrated with photovoltaic panel 2020 Amsterdam [u.a.] (DE-627)ELV004269535 volume:149 year:2022 pages:0 https://doi.org/10.1016/j.optlaseng.2021.106819 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 50.70 Energie: Allgemeines VZ 83.65 Versorgungswirtschaft VZ 52.57 Energiespeicherung VZ 52.56 Regenerative Energieformen alternative Energieformen VZ AR 149 2022 0 |
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Elektronische Aufsätze |
| author-letter |
Wu, Huazheng |
| doi_str_mv |
10.1016/j.optlaseng.2021.106819 |
| dewey-full |
620 |
| title_sort |
single shot real-time high-resolution imaging through dynamic turbid media based on deep learning |
| title_auth |
Single shot real-time high-resolution imaging through dynamic turbid media based on deep learning |
| abstract |
• We scrutinize the Fourier-domain shower-curtain effect (FDSE)-related noise model, which makes it possible to simulate adequate training data to learn FDSE correlography problems. • Without knowing the experimental scene, the generated convolutional neural network (CNN) is computationally efficient and extremely robust to various forms of noise, far exceeding the capabilities of existing algorithms. • The targets can be reconstructed from a standard sCMOS detector with a 150 ms exposure. Optical experiments have proved that in dynamic turbid media, the real-time high-resolution imaging with a single lens is possible. |
| abstractGer |
• We scrutinize the Fourier-domain shower-curtain effect (FDSE)-related noise model, which makes it possible to simulate adequate training data to learn FDSE correlography problems. • Without knowing the experimental scene, the generated convolutional neural network (CNN) is computationally efficient and extremely robust to various forms of noise, far exceeding the capabilities of existing algorithms. • The targets can be reconstructed from a standard sCMOS detector with a 150 ms exposure. Optical experiments have proved that in dynamic turbid media, the real-time high-resolution imaging with a single lens is possible. |
| abstract_unstemmed |
• We scrutinize the Fourier-domain shower-curtain effect (FDSE)-related noise model, which makes it possible to simulate adequate training data to learn FDSE correlography problems. • Without knowing the experimental scene, the generated convolutional neural network (CNN) is computationally efficient and extremely robust to various forms of noise, far exceeding the capabilities of existing algorithms. • The targets can be reconstructed from a standard sCMOS detector with a 150 ms exposure. Optical experiments have proved that in dynamic turbid media, the real-time high-resolution imaging with a single lens is possible. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
| title_short |
Single shot real-time high-resolution imaging through dynamic turbid media based on deep learning |
| url |
https://doi.org/10.1016/j.optlaseng.2021.106819 |
| remote_bool |
true |
| author2 |
Meng, Xiangfeng Yang, Xiulun Li, Xianye Yin, Yongkai |
| author2Str |
Meng, Xiangfeng Yang, Xiulun Li, Xianye Yin, Yongkai |
| ppnlink |
ELV004269535 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth |
| doi_str |
10.1016/j.optlaseng.2021.106819 |
| up_date |
2024-07-06T18:18:46.731Z |
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1803854730211885057 |
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7.401125 |