基于深度学习的透过散射介质偏振识别

Polarization Recognition Through Scattering Media Based on Deep-Learning

当偏振光在散射介质中传播时,由于散射粒子的多重散射作用而造成偏振信息的扰乱和丢失。为了保证偏振信息在散射介质中高效和高保真的传输,提出一种基于深度学习的透过散射介质偏振识别方法。通过构建卷积神经网络来提取散斑光强信息中入射光波偏振信息的特征,实现对入射光波偏振态的高分辨率识别,并使用初始相位不同的偏振光来验证卷积神经网络对偏振态识别的鲁棒性。实验结果表明,所提方法具有识别速度快和准确率高的优点,理论上可以通过无限大的数据来训练神经网络,因此该方法在偏振光学成像和激光通信等领域具有巨大的应用潜力。

When polarized light propagates in the scattering medium,the polarization information is disturbed and lost due to multiple scattering of scattering particles.In order to ensure efficient and high-fidelity transmission of polarization information in scattering media,a polarization recognition method through scattering media based on deep learning is proposed.A convolutional neural network is constructed to extract the characteristics of the polarization information of incident light wave from the speckle light intensity information to realize the high resolution recognition of the polarization state of incident light wave,and the robustness of the convolutional neural network for polarization state recognition is verified by using polarized light with different initial phases.Experimental results show that the proposed method has the advantages of fast recognition speed and high accuracy,and the neural network can be trained with infinite data in theory.Therefore,the method has great application potential in polarization optical imaging and laser communication.