Ziqiang Li, Xinyang Li, Zeyu Gao, Qiwang Jia. Review of wavefront sensing technology in adaptive optics based on deep learning[J]. High Power Laser and Particle Beams, 2021, 33(8): 081001
- High Power Laser and Particle Beams
- Vol. 33, Issue 8, 081001 (2021)
Fig. 1. Development of Deep Learning
Fig. 2. ANN and the basic operation of a neuron
Fig. 3. Optical path of using wavefront sensor based on ANN to correct the aberration of two independent mirrors of MMT
Fig. 4. Architecture of wavefront sensor based on ANN
Fig. 5. Result of the PD model of PD-CNN
Fig. 6. Sketch (a) and physical map (b) of the optical system used in the experiment
Fig. 7. Elongation of spots in the SHWFS. Here, r is the distance of the subaperture in the SHWFS from the center (as projected on to the primary mirror), h 0 is the average altitude of the sodium layer, σ NA is the thickness of the sodium layer and z is the zenith angle
Fig. 8. Experimental result of centroid computation for Shack-Hartmann wavefront sensor in extreme situations based on the SHNN
Fig. 9. Wavefront sensing by using multiple laser guide stars
Fig. 10. CARMEN with MLP
Fig. 11. Example of the topology of CARMEN with CNN
Fig. 12. ISNet architecture
Fig. 13. Statistical results of the RMS wavefront error of five methods in wavefront detection. Each kind of phase screen contains 100 datasets
Fig. 14. Physical process and flow chart of the CS-WFS method
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Table 1. Results of experiments
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