Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81348
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dc.contributorDepartment of Biomedical Engineering-
dc.creatorCheng, SF-
dc.creatorLi, HH-
dc.creatorLuo, YQ-
dc.creatorZheng, YJ-
dc.creatorLai, PX-
dc.date.accessioned2019-09-20T00:55:08Z-
dc.date.available2019-09-20T00:55:08Z-
dc.identifier.issn1793-5458en_US
dc.identifier.urihttp://hdl.handle.net/10397/81348-
dc.language.isoenen_US
dc.publisherWorld Scientificen_US
dc.rights© The Author(s)en_US
dc.rightsThis is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY) License (https://creativecommons.org/licenses/by/4.0/) which permits use, distribution and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe following publication Cheng, S. F., Li, H. H., Luo, Y. Q., Zheng, Y. J., & Lai, P. X. (2019). Artificial intelligence-assisted light control and computational imaging through scattering media. Journal of Innovative Optical Health Sciences, 12(4), 1930006, 1-14 is available at https://dx.doi.org/10.1142/S1793545819300064en_US
dc.subjectOptical scatteringen_US
dc.subjectDeep learningen_US
dc.subjectWavefront shapingen_US
dc.subjectAdaptive opticsen_US
dc.subjectComputational imagingen_US
dc.titleArtificial intelligence-assisted light control and computational imaging through scattering mediaen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1en_US
dc.identifier.epage14en_US
dc.identifier.volume12en_US
dc.identifier.issue4en_US
dc.identifier.doi10.1142/S1793545819300064en_US
dcterms.abstractCoherent optical control within or through scattering media via wavefront shaping has seen broad applications since its invention around 2007. Wavefront shaping is aimed at overcoming the strong scattering, featured by random interference, namely speckle patterns. This randomness occurs due to the refractive index inhomogeneity in complex media like biological tissue or the modal dispersion in multimode fiber, yet this randomness is actually deterministic and potentially can be time reversal or precompensated. Various wavefront shaping approaches, such as optical phase conjugation, iterative optimization, and transmission matrix measurement, have been developed to generate tight and intense optical delivery or high-resolution image of an optical object behind or within a scattering medium. The performance of these modulations, however, is far from satisfaction. Most recently, artificial intelligence has brought new inspirations to this field, providing exciting hopes to tackle the challenges by mapping the input and output optical patterns and building a neuron network that inherently links them. In this paper, we survey the developments to date on this topic and briefly discuss our views on how to harness machine learning (deep learning in particular) for further advancements in the field.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of innovative optical health sciences, July 2019, v. 12, no. 4, 1930006, p. 1-14-
dcterms.isPartOfJournal of innovative optical health sciences-
dcterms.issued2019-
dc.identifier.isiWOS:000478637300004-
dc.identifier.scopus2-s2.0-85073901139-
dc.identifier.artn1930006en_US
dc.description.validate201909 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera0840-n08, OA_Scopus/WOSen_US
dc.identifier.SubFormID1795en_US
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextRGC: 25204416en_US
dc.description.fundingTextOthers: P0020260, P0020279, P0020352, P0012633en_US
dc.description.pubStatusPublisheden_US
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