Artificial intelligence-assisted light control and computational imaging through scattering media
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 r...
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| Ձևաչափ: | Journal Article |
| Լեզու: | English |
| Հրապարակվել է: |
2020
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| Առցանց հասանելիություն: | https://hdl.handle.net/10356/142849 |
| _version_ | 1826124075224793088 |
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| author | Cheng, Shengfu Li, Huanhao Luo, Yunqi Zheng, Yuanjin Lai, Puxiang |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Cheng, Shengfu Li, Huanhao Luo, Yunqi Zheng, Yuanjin Lai, Puxiang |
| author_sort | Cheng, Shengfu |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:14:44Z |
| format | Journal Article |
| id | ntu-10356/142849 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:14:44Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1428492020-07-03T08:29:38Z Artificial intelligence-assisted light control and computational imaging through scattering media Cheng, Shengfu Li, Huanhao Luo, Yunqi Zheng, Yuanjin Lai, Puxiang School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Optical Scattering Deep Learning 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. Published version 2020-07-03T08:29:38Z 2020-07-03T08:29:38Z 2019 Journal Article Cheng, S., Li, H., Luo, Y., Zheng, Y., & Lai, P. (2019). Artificial intelligence-assisted light control and computational imaging through scattering media. Journal of Innovative Optical Health Sciences, 12(4), 1930006-. doi:10.1142/s1793545819300064 1793-5458 https://hdl.handle.net/10356/142849 10.1142/S1793545819300064 2-s2.0-85073901139 4 12 en Journal of Innovative Optical Health Sciences © 2019 The Author(s). This is an Open Access article published by World Scientic Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited. application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Optical Scattering Deep Learning Cheng, Shengfu Li, Huanhao Luo, Yunqi Zheng, Yuanjin Lai, Puxiang Artificial intelligence-assisted light control and computational imaging through scattering media |
| title | Artificial intelligence-assisted light control and computational imaging through scattering media |
| title_full | Artificial intelligence-assisted light control and computational imaging through scattering media |
| title_fullStr | Artificial intelligence-assisted light control and computational imaging through scattering media |
| title_full_unstemmed | Artificial intelligence-assisted light control and computational imaging through scattering media |
| title_short | Artificial intelligence-assisted light control and computational imaging through scattering media |
| title_sort | artificial intelligence assisted light control and computational imaging through scattering media |
| topic | Engineering::Electrical and electronic engineering Optical Scattering Deep Learning |
| url | https://hdl.handle.net/10356/142849 |
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