Research on multi-point light focusing based on complex neural network

C. Xiang, Zhaoyang Ttang, Yuanshen Xiao, Yingchun Ding, Jiaqi He
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Abstract

The research of light focusing and imaging through scattering media is currently a popular topic. Many focusing technologies have been developed, such as transmission matrix method, phase conjugation method, iterative optimization method, etc. However, these methods have some limitations. At present, deep learning methods have been widely used in the field of image restoration, and have strong inverse restoration capabilities. Yet, the application of this method in the field of optical focusing is still relatively limited, and the performance is not ideal. In this letter, a method based on complex neural network is proposed, and the multi-point focusing of light passing through the scattering medium is numerically simulated. Since the complex information of the network is not reduced, compared with the real number neural network method, this method more accurately simulates the recovery process of light scattering, and can obtain multiple light focal points with high enhancement at the same time.
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基于复杂神经网络的多点光聚焦研究
散射介质对光聚焦成像的研究是当前研究的热点。许多聚焦技术被开发出来,如传输矩阵法、相位共轭法、迭代优化法等。然而,这些方法有一些局限性。目前,深度学习方法已广泛应用于图像恢复领域,并具有较强的逆恢复能力。然而,该方法在光学聚焦领域的应用还比较有限,性能也不理想。本文提出了一种基于复杂神经网络的方法,并对光在散射介质中的多点聚焦进行了数值模拟。由于没有减少网络的复杂信息,与实数神经网络方法相比,该方法更准确地模拟了光散射的恢复过程,并且可以同时获得多个高增强的光焦点。
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