Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/81348
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Biomedical Engineering | - |
dc.creator | Cheng, SF | - |
dc.creator | Li, HH | - |
dc.creator | Luo, YQ | - |
dc.creator | Zheng, YJ | - |
dc.creator | Lai, PX | - |
dc.date.accessioned | 2019-09-20T00:55:08Z | - |
dc.date.available | 2019-09-20T00:55:08Z | - |
dc.identifier.issn | 1793-5458 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/81348 | - |
dc.language.iso | en | en_US |
dc.publisher | World Scientific | en_US |
dc.rights | © The Author(s) | en_US |
dc.rights | This 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.rights | The 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/S1793545819300064 | en_US |
dc.subject | Optical scattering | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Wavefront shaping | en_US |
dc.subject | Adaptive optics | en_US |
dc.subject | Computational imaging | en_US |
dc.title | Artificial intelligence-assisted light control and computational imaging through scattering media | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1 | en_US |
dc.identifier.epage | 14 | en_US |
dc.identifier.volume | 12 | en_US |
dc.identifier.issue | 4 | en_US |
dc.identifier.doi | 10.1142/S1793545819300064 | en_US |
dcterms.abstract | Coherent 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.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of innovative optical health sciences, July 2019, v. 12, no. 4, 1930006, p. 1-14 | - |
dcterms.isPartOf | Journal of innovative optical health sciences | - |
dcterms.issued | 2019 | - |
dc.identifier.isi | WOS:000478637300004 | - |
dc.identifier.scopus | 2-s2.0-85073901139 | - |
dc.identifier.artn | 1930006 | en_US |
dc.description.validate | 201909 bcrc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | a0840-n08, OA_Scopus/WOS | en_US |
dc.identifier.SubFormID | 1795 | en_US |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | RGC: 25204416 | en_US |
dc.description.fundingText | Others: P0020260, P0020279, P0020352, P0012633 | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Cheng_Artificial_Intelligence-assisted_Light.pdf | 828.77 kB | Adobe PDF | View/Open |
Page views
345
Last Week
0
0
Last month
Citations as of Mar 24, 2024
Downloads
237
Citations as of Mar 24, 2024
SCOPUSTM
Citations
33
Citations as of Mar 28, 2024
WEB OF SCIENCETM
Citations
29
Citations as of Mar 28, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.