Turbulence aberration correction for vector vortex beams using deep neural networks on experimental data
| Autor: | Shiyao Fu, Jianqiang Zhang, Xueting Liu, Heng Zhou, Yanwang Zhai, Chunqing Gao |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Physics
Angular momentum Turbulence Zernike polynomials business.industry 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Atomic and Molecular Physics and Optics Vortex 010309 optics symbols.namesake Light intensity Optics 0103 physical sciences symbols Light beam 0210 nano-technology business Adaptive optics Light field |
| Zdroj: | Optics express. 28(5) |
| ISSN: | 1094-4087 |
| Popis: | The vector vortex beams (VVB) possessing non-separable states of light, in which polarization and orbital angular momentum (OAM) are coupled, have attracted more and more attentions in science and technology, due to the unique nature of the light field. However, atmospheric transmission distortion is a recurring challenge hampering the practical application, such as communication and imaging. In this work, we built a deep learning based adaptive optics system to compensate the turbulence aberrations of the vector vortex mode in terms of phase distribution and mode purity. A turbulence aberration correction convolutional neural network (TACCNN) model, which can learn the mapping relationship of intensity profile of the distorted vector vortex modes and the turbulence phase generated by first 20 Zernike modes, is well designed. After supervised learning plentiful experimental samples, the TACCNN model compensates turbulence aberration for VVB quickly and accurately. For the first time, experimental results show that through correction, the mode purity of the distorted VVB improves from 19% to 70% under the turbulence strength of D/r0 = 5.28 with correction time 100 ms. Furthermore, both spatial modes and the light intensity distribution can be well compensated in different atmospheric turbulence. |
| Databáze: | OpenAIRE |
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