Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in the optical path. We demonstrate an optical diffractive decoder with electronic encoding to accurately transfer the optical information of interest, corresponding to, e.g., any arbitrary input object or message, through unknown random phase diffusers along the optical path. This hybrid electronic-optical model, trained using supervised learning, comprises a convolutional neural network-based electronic encoder and successive passive diffractive layers that are jointly optimized. After their joint training using deep learning, our hybrid model can transfer optical information through unknown phase diffusers, demonstrating generalization to new random diffusers never seen before. The resulting electronic-encoder and optical-decoder model was experimentally validated using a 3D-printed diffractive network that axially spans <70λ, where λ = 0.75 mm is the illumination wavelength in the terahertz spectrum, carrying the desired optical information through random unknown diffusers. The presented framework can be physically scaled to operate at different parts of the electromagnetic spectrum, without retraining its components, and would offer low-power and compact solutions for optical information transfer in free space through unknown random diffusive media.

提出了一种基于轨道角动量全息(Orbital Angular Momentum, OAM)和频移的大容量光学信息加密方法。该方法实现了对多个图像信息的并行加密。首先，对多幅原始图像进行采样，采样阵列的采样间隔取决于具有不同拓扑荷数的螺旋相位的空间频率。然后，多个采样图像信息经过随机相位调制、傅里叶变换和频移相位调制后相干叠加构成轨道角动量保留全息图。最后，将不同拓扑荷的螺旋相位分别编码到轨道角动量保留全息图中，得到轨道角动量选择全息图，进行相干叠加后构成最终的单个加密全息图。解密时，轨道角动量复合选择全息图被加载到空间光调制器上，用包含特定拓扑荷数的涡旋光束照射，并经过傅里叶变换获得多个解密信息。该加密系统具有极高的加密灵活性和极大的加密容量，不仅可以在同一拓扑荷下，设计不同的频移因子来并行加密一组多个图像信息，还可以利用不同拓扑荷对多组图像信息进行加密。该方法将涡旋光束的模式设定为一个新的光学密钥，极大地提高了光学加密系统的安全性。此外，该光学加密方法中，待加密图像信息的尺寸不受空间光调制器的像元数量限制，极大地提高了光学实现信息加密的可行性和有效性。仿真实验结果表明该方法具有较高的安全性、抗噪性和抗剪切能力。