How convolutional-neural-network detects optical vortex scattering fields. (January 2023)

• Record Type: Journal Article
• Authors: Hu, Junbao; Guo, Zefeng; Fu, Yuhui; Gan, Jia-An; Chen, Peng-Fei; Chen, Guangyong; Min, Changjun; Yuan, Xiaocong; Feng, Fu
• Is Part Of: Optics and lasers in engineering. Volume 160(2023)
• Publication Date: 2023-01
• Subjects: Optical vortex -- orbital angular momentum -- scattering -- convolutional neural network; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2022.107246
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: The physical mechanism of CNN fulfilling scattered OAM detection is researched. We demonstrate that CNN converts the speckle to incident OAM TC values relies on phase front. We demonstrate that CNN is able to learn intrinsic features of a specific disordered medium. We demonstrate that CNN can learn the discontinuity features of the fractional OAM with phase shifts. Abstract: Light scattering through disordered media is a critical topic in optical engineering as its ubiquity in natural and artificial systems. Recent progress has shown that deep learning is capable to recognize topological charge values carried by orbital angular momentum (OAM) waves with ultra-fine resolution under scattering environment. However, the physical mechanism of how a deep learning convolutional neural network (CNN) fulfills such tasks remains unclear. In this paper, in perspective of optical vortex scattering field detection, we studied the basic physical mechanism of the CNN on recognizing scattered vortex beams carrying OAMs. It has been demonstrated that a CNN uses statistical invariance of both spatial phase front of an incident OAM wave and intrinsic features of a specific disordered medium across large-scale datasets to identify the OAM topological charge values from speckles. This work can provide insightful reference for CNN-assisted OAM-based scattering detection.