深度学习作为机器学习的重要分支, 自出现之初就掀起了机器学习的又一次高潮。深度学习在诸如图像识别与分类、语义分割、智能驾驶等多个领域有着优异的表现。同时, 深度学习算法以其抽象特征识别和提取特性, 极强的模型构建和泛化推广能力, 被广泛应用于光学领域, 如计算全息图产生与成像、数字全息的无参数重建和光谱共振曲线预测等方面。详细介绍了深度学习的基本原理及在图像分类、超分辨成像、计算全息和数字全息、表面等离激元共振曲线预测、超表面的结构设计等方面的典型应用研究, 并探讨了深度学习在物理光学领域未来值得研究的方向。

As an important branch of machine learning, deep learning has reached another climax of machine learning since its inception. Deep learning has excellent performance in many fields such as image recognition and classification, semantic segmentation, and intelligent driving and so on. At the same time, deep learning algorithms are widely used in the field of optics such as computational hologram generation and imaging, non-parameter reconstruction of digital holography, and spectral resonance curves prediction due to their abstract feature recognition and extraction characteristics, strong model building and generalization capabilities. This article detailed the basic principles of deep learning and its typical application research in image classification, super-resolution imaging, computer generated hologram and digital holography, prediction of surface plasmonics resonance curves, and structural design of metasurfaces. And future development of deep learning in the physical optical field was worth exploring.