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Article: Learning-based nonparametric autofocusing for digital holography
Title | Learning-based nonparametric autofocusing for digital holography |
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Authors | |
Issue Date | 2018 |
Publisher | Optical Society of America. The Journal's web site is located at https://www.osapublishing.org/optica/home.cfm |
Citation | Optica, 2018, v. 5 n. 4, p. 337-344 How to Cite? |
Abstract | In digital holography, it is crucial to extract the object distance from a hologram in order to reconstruct its amplitude and phase. This is known as autofocusing, which is conventionally solved by first reconstructing a stack of images, and then the sharpness of each reconstructed image is computed using a focus metric such as entropy or variance. The distance corresponding to the sharpest image is considered the focal position. This approach, while effective, is computationally demanding and time-consuming. To cope with this problem, we turn to machine learning, where we cast the autofocusing as a regression problem, with the focal distance being a continuous response corresponding to each hologram. Therefore, distance estimation is converted to hologram prediction, which we solve by designing a powerful convolutional neural network trained by a set of holograms acquired a priori. Experimental results show that this allows fast autofocusing without reconstructing an image stack, even when the physical parameters of the optical setup are unknown. |
Persistent Identifier | http://hdl.handle.net/10722/259275 |
ISSN | 2023 Impact Factor: 8.4 2023 SCImago Journal Rankings: 3.549 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ren, Z | - |
dc.contributor.author | Xu, Z | - |
dc.contributor.author | Lam, EYM | - |
dc.date.accessioned | 2018-09-03T04:04:17Z | - |
dc.date.available | 2018-09-03T04:04:17Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Optica, 2018, v. 5 n. 4, p. 337-344 | - |
dc.identifier.issn | 2334-2536 | - |
dc.identifier.uri | http://hdl.handle.net/10722/259275 | - |
dc.description.abstract | In digital holography, it is crucial to extract the object distance from a hologram in order to reconstruct its amplitude and phase. This is known as autofocusing, which is conventionally solved by first reconstructing a stack of images, and then the sharpness of each reconstructed image is computed using a focus metric such as entropy or variance. The distance corresponding to the sharpest image is considered the focal position. This approach, while effective, is computationally demanding and time-consuming. To cope with this problem, we turn to machine learning, where we cast the autofocusing as a regression problem, with the focal distance being a continuous response corresponding to each hologram. Therefore, distance estimation is converted to hologram prediction, which we solve by designing a powerful convolutional neural network trained by a set of holograms acquired a priori. Experimental results show that this allows fast autofocusing without reconstructing an image stack, even when the physical parameters of the optical setup are unknown. | - |
dc.language | eng | - |
dc.publisher | Optical Society of America. The Journal's web site is located at https://www.osapublishing.org/optica/home.cfm | - |
dc.relation.ispartof | Optica | - |
dc.rights | Optica. Copyright © Optical Society of America. | - |
dc.title | Learning-based nonparametric autofocusing for digital holography | - |
dc.type | Article | - |
dc.identifier.email | Lam, EYM: elam@eee.hku.hk | - |
dc.identifier.authority | Lam, EYM=rp00131 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1364/OPTICA.5.000337 | - |
dc.identifier.scopus | eid_2-s2.0-85045995814 | - |
dc.identifier.hkuros | 288775 | - |
dc.identifier.volume | 5 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 337 | - |
dc.identifier.epage | 344 | - |
dc.identifier.isi | WOS:000430601100003 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 2334-2536 | - |