Author Affiliations
Abstract
1 Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200093, China
2 School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
3 Nokia Shanghai Bell Co., Ltd., Shanghai 201206, China
4 College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
Edge detection for low-contrast phase objects cannot be performed directly by the spatial difference of intensity distribution. In this work, an all-optical diffractive neural network (DPENet) based on the differential interference contrast principle to detect the edges of phase objects in an all-optical manner is proposed. Edge information is encoded into an interference light field by dual Wollaston prisms without lenses and light-speed processed by the diffractive neural network to obtain the scale-adjustable edges. Simulation results show that DPENet achieves F-scores of 0.9308 (MNIST) and 0.9352 (NIST) and enables real-time edge detection of biological cells, achieving an F-score of 0.7462.
diffractive neural network edge detection phase objects Chinese Optics Letters
2024, 22(1): 011102
西安科技大学 通信与信息工程学院, 西安 710600
针对OAM通信系统中相干OAM复用光束的解调技术,提出了一种基于纯振幅型衍射深度神经网络(D2NN)的OAM相干复用解调实现方法。通过数值实验研究了D2NN解调器对四相OAM相干复用波束的解调性能,使用误码率（BER）对其性能进行了表征。为了降低D2NN解调的误码率,提出了一种改进的OAM选择策略。并与纯相位型D2NN解调器进行性能对比,仿真实验结果表明,该方法对四相OAM相干复用波束具有较高的解复用和解调精度有着明显优势,为OAM相干复用通信提供了一种灵活的实时解调方法。
轨道角动量 相干复用 衍射深度神经网络 解调 机器学习 orbital angular momentum coherent multiplexing deep diffractive neural network demodulation machine learning
Yuhang Li 1,2,3Tianyi Gan 1,3Bijie Bai 1,2,3Çağatay Işıl 1,2,3[ ... ]Aydogan Ozcan 1,2,3,*
Author Affiliations
Abstract
1 University of California, Department of Electrical and Computer Engineering, Los Angeles, California, United States
2 University of California, Department of Bioengineering, Los Angeles, California, United States
3 University of California, California NanoSystems Institute, Los Angeles, California, United States
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.
optical information transfer electronic encoding optical decoder diffractive neural network diffusers Advanced Photonics
2023, 5(4): 046009
