Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82097
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Title: Focusing light through scattering media by reinforced hybrid algorithms
Authors: Luo, Y
Yan, S
Li, H 
Lai, P 
Zheng, Y
Issue Date: Jan-2020
Source: APL photonics, Jan. 2020, v. 5, no. 1, 016109, p. 016109-1-016109-12
Abstract: Light scattering inside disordered media poses a significant challenge to achieve deep depth and high resolution simultaneously in biomedical optical imaging. Wavefront shaping emerged recently as one of the most potential methods to tackle this problem. So far, numerous algorithms have been reported, while each has its own pros and cons. In this article, we exploit a new thought that one algorithm can be reinforced by another complementary algorithm since they effectively compensate each other's weaknesses, resulting in a more efficient hybrid algorithm. Herein, we introduce a systematical approach named GeneNN (Genetic Neural Network) as a proof of concept. Preliminary light focusing has been achieved by a deep neural network, whose results are fed to a genetic algorithm as an initial condition. The genetic algorithm furthers the optimization, evolving to converge into the global optimum. Experimental results demonstrate that with the proposed GeneNN, optimization speed is almost doubled and wavefront shaping performance can be improved up to 40% over conventional methods. The reinforced hybrid algorithm shows great potential in facilitating various biomedical and optical imaging techniques.
Publisher: AIP Publishing
Journal: APL photonics 
EISSN: 2378-0967
DOI: 10.1063/1.5131181
Rights: © 2020 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Luo, Y., Yan, S., Li, H., Lai, P., & Zheng, Y. (2020). Focusing light through scattering media by reinforced hybrid algorithms. APL Photonics, 5(1), 016109-1-016109-12, is available at https://doi.org/10.1063/1.5131181
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