Broadband and parallel multiple-order optical spatial differentiation enabled by Bessel vortex modulated metalens
Optical analog image processing technology is expected to provide an effective solution for high-throughput and real-time data processing with low power consumption. In various operations, optical spatial differential operations are essential in edge extraction, data compression, and feature classification. Unfortunately, existing methods can only perform low-order or selectively perform a particular high-order differential operation. Here, we propose and experimentally demonstrate a Bessel vortex modulated metalens composed of a single complex amplitude metasurface, which can perform multiple-order radial differential operations over a wide band by presetting the order of the corresponding Bessel vortex. This architecture further enables angle multiplexing to create multiple information channels that synchronously perform multi-order spatial differential operations, indicating the superiority of the proposed devices in parallel processing. Our approach may find various applications in artificial intelligence, machine vision, autonomous driving, and advanced biomedical imaging.
Authors
- Yanqing Lu (ORCID: https://orcid.org/0000-0001-6151-8557)
- Haiyang Ren (ORCID: https://orcid.org/0000-0001-7965-4485)
- Yaqi Jin
- Peicheng Lin (ORCID: https://orcid.org/0000-0002-0126-1363)
- Ting Xu (ORCID: https://orcid.org/0000-0002-0704-1089)
- Pengcheng Huo (ORCID: https://orcid.org/0000-0003-1630-7608)
- Mingze Liu (ORCID: https://orcid.org/0009-0000-6569-382X)
- Song Zhang (ORCID: https://orcid.org/0000-0001-6685-8673)
- Yilin Wang (ORCID: https://orcid.org/0000-0002-9769-8649)
- Yanzeng Zhang (ORCID: https://orcid.org/0000-0001-7994-8352)
- Le Tan
Institutions
- China Pharmaceutical University (CN)
- Nanjing University of Science and Technology (CN)
- Collaborative Innovation Center of Advanced Microstructures (CN)
- Jiangsu University of Technology (CN)
- Changzhou University (CN)
- Nanjing University (CN)
Publication Details
- Journal
- Nature Communications
- Published
- 2024-10-19
- DOI
- https://doi.org/10.1038/s41467-024-53463-3
- Citations
- 49
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 17.02
Funders
- National Natural Science Foundation of China
- Government of Jiangsu Province
- Natural Science Foundation of Jiangsu Province
- Fundamental Research Funds for the Central Universities