Fan Minyu, Zhu Jie, Wang Shutong, Pu Yongjie, Li Huinan, Zhou Shouhuan, Wang Sha
Opt Express. 2022 Dec 19;30(26):46888-46899. doi: 10.1364/OE.476255.
Focusing light through scattering media is essential for high-resolution optical imaging and deep penetration. Here, a two-step focusing method based on neural networks (NNs) and multi-pixel coding is proposed to achieve high-quality focusing with theoretical maximum enhancement. In the first step, a single-layer neural network (SLNN) is used to obtain the initial mask, which can be used to focus with a moderate enhancement. In the second step, we use multi-pixel coding to encode the initial mask. The coded masks and their corresponding speckle patterns are used to train another SLNN to get the final mask and achieve high-quality focusing. In this experiment, for a mask of 16 × 16 modulation units, in the case of using 8 pixels in a modulation unit, focus with the enhancement of 40.3 (only 0.44 less than the theoretical value) has been achieved with 3000 pictures (1000 pictures in the first step and 2000 pictures in the second step). Compared with the case of employing only the initial mask and the direct multi-pixel encoded mask, the enhancement is increased by 220% and 24%. The proposed method provides a new idea for improving the focusing effect through the scattering media using NNs.
通过散射介质聚焦光线对于高分辨率光学成像和深度穿透至关重要。在此，提出了一种基于神经网络（NNs）和多像素编码的两步聚焦方法，以实现具有理论最大增强效果的高质量聚焦。第一步，使用单层神经网络（SLNN）获得初始掩模，该掩模可用于以适度增强效果进行聚焦。第二步，我们使用多像素编码对初始掩模进行编码。编码后的掩模及其相应的散斑图案用于训练另一个SLNN以获得最终掩模并实现高质量聚焦。在本实验中，对于一个具有16×16个调制单元的掩模，在每个调制单元使用8个像素的情况下，通过3000张图片（第一步1000张图片，第二步2000张图片）实现了40.3的增强聚焦（仅比理论值小0.44）。与仅使用初始掩模和直接多像素编码掩模的情况相比，增强效果分别提高了220%和24%。所提出的方法为利用神经网络通过散射介质改善聚焦效果提供了新思路。
