High-Resolution Recognition of Orbital Angular Momentum Modes in Asymmetric Bessel Beams Assisted by Deep Learning

Fractional orbital angular momentum(OAM) vortex beams present a promising way to increase the data throughput in optical communication systems. Nevertheless, high-precision recognition of fractional OAM with different propagation distances remains a significant challenge. We develop a convolutional neural network(CNN)method to realize high-resolution recognition of OAM modalities, leveraging asymmetric Bessel beams imbued with fractional OAM. Experimental results prove that our method achieves a recognition accuracy exceeding 94.3% for OAM modes, with an interval of 0.05, and maintains a high recognition accuracy above 92% across varying propagation distances. The findings of our research will be poised to significantly contribute to the deployment of fractional OAM beams within the domain of optical communications.

Harnessing the magic of light:spatial coherence instructed swin transformer for universal holographic imaging

.Holographic imaging poses significant challenges when facing real-time disturbances introduced by dynamic environments.The existing deep-learning methods for holographic imaging often depend solely on the specific condition based on the given data distributions,thus hindering their generalization across multiple scenes.One critical problem is how to guarantee the alignment between any given downstream tasks and pretrained models.We analyze the physical mechanism of image degradation caused by turbulence and innovatively propose a swin transformer-based method,termed train-with-coherence-swin(TWC-Swin)transformer,which uses spatial coherence(SC)as an adaptable physical prior information to precisely align image restoration tasks in the arbitrary turbulent scene.The light-processing system(LPR)we designed enables manipulation of SC and simulation of any turbulence.Qualitative and quantitative evaluations demonstrate that the TWC-Swin method presents superiority over traditional convolution frameworks and realizes image restoration under various turbulences,which suggests its robustness,powerful generalization capabilities,and adaptability to unknown environments.Our research reveals the significance of physical prior information in the optical intersection and provides an effective solution for model-to-tasks alignment schemes,which will help to unlock the full potential of deep learning for all-weather optical imaging across terrestrial,marine,and aerial domains.

Multiplication and division of orbital angular momentum beams by Fermat's spiral transformation

Optical geometrical transformation is a novel and powerful tool to switch orbital angular momentum (OAM)states in modern optics. We demonstrate a scheme to operate multiplication and division in OAM by Fermat’s spiral transformation. The characteristics of the output beams in the case of integer and fraction OAM operations are presented in detail. Additionally, the power weight of the output OAM modes and the interference patterns of the output beams are reported to confirm the expected ability of OAM mode conversion by Fermat’s spiral transformation. We further investigate the evolution of OAM beams in operations theoretically and experimentally.This work provides a practical way to perform an optical transformation mapping on OAM beams. It can find application in optical communications with larger OAM mode numbers as well as quantum information in high-dimensional systems.