机器学习遇见奇异光学：基于散斑的结构光解复用
Machine learning meets Singular Optics: Speckle-based Structured light demultiplexing
AI总结：
本文提出基于散斑场和CNN的结构光解复用方法，可在高噪声、局部散斑及离轴条件下识别结构光束，摆脱直接检测对光束方向和对准的依赖。
摘要：
本文介绍了利用基于散斑的卷积神经网络（CNN）识别结构光束的研究进展。散斑场由多个波前经漫射体衍射和散射后发生干涉而产生，呈现出一种随机分布。这种生成的相位与强度随机分布与相应散斑场的结构光束相关联。相位和强度的独特分布为识别结构光中编码的信息提供了额外维度。CNN非常适合利用这种独特能力，通过学习数据中的隐藏模式来识别散斑场。基于散斑识别的一个显著优势是，即使在高噪声环境中，也能够根据散斑场的一小部分识别结构光束。散斑场的衍射特性使离轴识别成为可能，展示了其利用结构光束进行信息广播的能力。这显著区别于基于直接模式检测的模型，转向了无需对准的基于散斑的检测模型，后者不再受到激光束方向性的约束。
英文摘要：
In this paper, the advancements in structured light beams recognition using speckle-based convolutional neural networks (CNNs) have been presented. Speckle fields, generated by the interference of multiple wavefronts diffracted and scattered through a diffuser, project a random distribution. The generated random distribution of phase and intensity correlates to the structured light beam of the corresponding speckle field. This unique distribution of phase and intensity offers an additional dimension for recognizing the encoded information in structured light. The CNNs are well-suited for harnessing this unique ability to recognize the speckle field by learning hidden patterns within data. One notable advantage of speckle-based recognition is their ability to identify structured light beams from a small portion of the speckle field, even in high-noise environments. The diffractive nature of the speckle field enables off-axis recognition, showcasing its capability in information broadcasting employing structured light beams. This is a significant departure from direct-mode detection-based models to alignment-free speckle-based detection models, which are no longer constrained by the directionality of laser beams.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
