Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142849
Title: Artificial intelligence-assisted light control and computational imaging through scattering media
Authors: Cheng, Shengfu
Li, Huanhao
Luo, Yunqi
Zheng, Yuanjin
Lai, Puxiang
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: 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
Journal: Journal of Innovative Optical Health Sciences
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.
URI: https://hdl.handle.net/10356/142849
ISSN: 1793-5458
DOI: 10.1142/S1793545819300064
Rights: © 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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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