Lensless Three-Dimensional Imaging under Photon-Starved Conditions

In this paper, we propose a lensless three-dimensional (3D) imaging under photon-starved conditions using diffraction grating and computational photon counting method. In conventional 3D imaging with and without the lens, 3D visualization of objects under photon-starved conditions may be difficult d...
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Gespeichert in:

Autor*in:	Jae-Young Jang [verfasserIn] Myungjin Cho [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Bayesian estimation computational photon counting method diffraction grating imaging lensless 3D imaging maximum likelihood estimation multiple observation photon counting method

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 23(2023), 4, p 2336
Übergeordnetes Werk:	volume:23 ; year:2023 ; number:4, p 2336

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s23042336

Katalog-ID:	DOAJ079977553

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520			\|a In this paper, we propose a lensless three-dimensional (3D) imaging under photon-starved conditions using diffraction grating and computational photon counting method. In conventional 3D imaging with and without the lens, 3D visualization of objects under photon-starved conditions may be difficult due to lack of photons. To solve this problem, our proposed method uses diffraction grating imaging as lensless 3D imaging and computational photon counting method for 3D visualization of objects under these conditions. In addition, to improve the visual quality of 3D images under severely photon-starved conditions, in this paper, multiple observation photon counting method with advanced statistical estimation such as Bayesian estimation is proposed. Multiple observation photon counting method can estimate the more accurate 3D images by remedying the random errors of photon occurrence because it can increase the samples of photons. To prove the ability of our proposed method, we implement the optical experiments and calculate the peak sidelobe ratio as the performance metric.
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allfieldsGer	10.3390/s23042336 doi (DE-627)DOAJ079977553 (DE-599)DOAJa9e687a8772a4846b9b4e62d9a4a5696 DE-627 ger DE-627 rakwb eng TP1-1185 Jae-Young Jang verfasserin aut Lensless Three-Dimensional Imaging under Photon-Starved Conditions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we propose a lensless three-dimensional (3D) imaging under photon-starved conditions using diffraction grating and computational photon counting method. In conventional 3D imaging with and without the lens, 3D visualization of objects under photon-starved conditions may be difficult due to lack of photons. To solve this problem, our proposed method uses diffraction grating imaging as lensless 3D imaging and computational photon counting method for 3D visualization of objects under these conditions. In addition, to improve the visual quality of 3D images under severely photon-starved conditions, in this paper, multiple observation photon counting method with advanced statistical estimation such as Bayesian estimation is proposed. Multiple observation photon counting method can estimate the more accurate 3D images by remedying the random errors of photon occurrence because it can increase the samples of photons. To prove the ability of our proposed method, we implement the optical experiments and calculate the peak sidelobe ratio as the performance metric. Bayesian estimation computational photon counting method diffraction grating imaging lensless 3D imaging maximum likelihood estimation multiple observation photon counting method Chemical technology Myungjin Cho verfasserin aut In Sensors MDPI AG, 2003 23(2023), 4, p 2336 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:4, p 2336 https://doi.org/10.3390/s23042336 kostenfrei https://doaj.org/article/a9e687a8772a4846b9b4e62d9a4a5696 kostenfrei https://www.mdpi.com/1424-8220/23/4/2336 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 4, p 2336
allfieldsSound	10.3390/s23042336 doi (DE-627)DOAJ079977553 (DE-599)DOAJa9e687a8772a4846b9b4e62d9a4a5696 DE-627 ger DE-627 rakwb eng TP1-1185 Jae-Young Jang verfasserin aut Lensless Three-Dimensional Imaging under Photon-Starved Conditions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we propose a lensless three-dimensional (3D) imaging under photon-starved conditions using diffraction grating and computational photon counting method. In conventional 3D imaging with and without the lens, 3D visualization of objects under photon-starved conditions may be difficult due to lack of photons. To solve this problem, our proposed method uses diffraction grating imaging as lensless 3D imaging and computational photon counting method for 3D visualization of objects under these conditions. In addition, to improve the visual quality of 3D images under severely photon-starved conditions, in this paper, multiple observation photon counting method with advanced statistical estimation such as Bayesian estimation is proposed. Multiple observation photon counting method can estimate the more accurate 3D images by remedying the random errors of photon occurrence because it can increase the samples of photons. To prove the ability of our proposed method, we implement the optical experiments and calculate the peak sidelobe ratio as the performance metric. Bayesian estimation computational photon counting method diffraction grating imaging lensless 3D imaging maximum likelihood estimation multiple observation photon counting method Chemical technology Myungjin Cho verfasserin aut In Sensors MDPI AG, 2003 23(2023), 4, p 2336 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:4, p 2336 https://doi.org/10.3390/s23042336 kostenfrei https://doaj.org/article/a9e687a8772a4846b9b4e62d9a4a5696 kostenfrei https://www.mdpi.com/1424-8220/23/4/2336 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 4, p 2336
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source	In Sensors 23(2023), 4, p 2336 volume:23 year:2023 number:4, p 2336
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topic_facet	Bayesian estimation computational photon counting method diffraction grating imaging lensless 3D imaging maximum likelihood estimation multiple observation photon counting method Chemical technology
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authorswithroles_txt_mv	Jae-Young Jang @@aut@@ Myungjin Cho @@aut@@
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callnumber-first	T - Technology
author	Jae-Young Jang
spellingShingle	Jae-Young Jang misc TP1-1185 misc Bayesian estimation misc computational photon counting method misc diffraction grating imaging misc lensless 3D imaging misc maximum likelihood estimation misc multiple observation photon counting method misc Chemical technology Lensless Three-Dimensional Imaging under Photon-Starved Conditions
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topic_title	TP1-1185 Lensless Three-Dimensional Imaging under Photon-Starved Conditions Bayesian estimation computational photon counting method diffraction grating imaging lensless 3D imaging maximum likelihood estimation multiple observation photon counting method
topic	misc TP1-1185 misc Bayesian estimation misc computational photon counting method misc diffraction grating imaging misc lensless 3D imaging misc maximum likelihood estimation misc multiple observation photon counting method misc Chemical technology
topic_unstemmed	misc TP1-1185 misc Bayesian estimation misc computational photon counting method misc diffraction grating imaging misc lensless 3D imaging misc maximum likelihood estimation misc multiple observation photon counting method misc Chemical technology
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title	Lensless Three-Dimensional Imaging under Photon-Starved Conditions
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title_sort	lensless three-dimensional imaging under photon-starved conditions
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title_auth	Lensless Three-Dimensional Imaging under Photon-Starved Conditions
abstract	In this paper, we propose a lensless three-dimensional (3D) imaging under photon-starved conditions using diffraction grating and computational photon counting method. In conventional 3D imaging with and without the lens, 3D visualization of objects under photon-starved conditions may be difficult due to lack of photons. To solve this problem, our proposed method uses diffraction grating imaging as lensless 3D imaging and computational photon counting method for 3D visualization of objects under these conditions. In addition, to improve the visual quality of 3D images under severely photon-starved conditions, in this paper, multiple observation photon counting method with advanced statistical estimation such as Bayesian estimation is proposed. Multiple observation photon counting method can estimate the more accurate 3D images by remedying the random errors of photon occurrence because it can increase the samples of photons. To prove the ability of our proposed method, we implement the optical experiments and calculate the peak sidelobe ratio as the performance metric.
abstractGer	In this paper, we propose a lensless three-dimensional (3D) imaging under photon-starved conditions using diffraction grating and computational photon counting method. In conventional 3D imaging with and without the lens, 3D visualization of objects under photon-starved conditions may be difficult due to lack of photons. To solve this problem, our proposed method uses diffraction grating imaging as lensless 3D imaging and computational photon counting method for 3D visualization of objects under these conditions. In addition, to improve the visual quality of 3D images under severely photon-starved conditions, in this paper, multiple observation photon counting method with advanced statistical estimation such as Bayesian estimation is proposed. Multiple observation photon counting method can estimate the more accurate 3D images by remedying the random errors of photon occurrence because it can increase the samples of photons. To prove the ability of our proposed method, we implement the optical experiments and calculate the peak sidelobe ratio as the performance metric.
abstract_unstemmed	In this paper, we propose a lensless three-dimensional (3D) imaging under photon-starved conditions using diffraction grating and computational photon counting method. In conventional 3D imaging with and without the lens, 3D visualization of objects under photon-starved conditions may be difficult due to lack of photons. To solve this problem, our proposed method uses diffraction grating imaging as lensless 3D imaging and computational photon counting method for 3D visualization of objects under these conditions. In addition, to improve the visual quality of 3D images under severely photon-starved conditions, in this paper, multiple observation photon counting method with advanced statistical estimation such as Bayesian estimation is proposed. Multiple observation photon counting method can estimate the more accurate 3D images by remedying the random errors of photon occurrence because it can increase the samples of photons. To prove the ability of our proposed method, we implement the optical experiments and calculate the peak sidelobe ratio as the performance metric.
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title_short	Lensless Three-Dimensional Imaging under Photon-Starved Conditions
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Lensless Three-Dimensional Imaging under Photon-Starved Conditions

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