Zhiguang Zhang, Huizhen Yang, Jinlong Liu, Songheng Li, Hang Su, Yuxiang Luo, Xiewen Wei. Research progress in deep learning based WFSless adaptive optics system[J]. High Power Laser and Particle Beams, 2021, 33(8): 081004
- High Power Laser and Particle Beams
- Vol. 33, Issue 8, 081004 (2021)
![Perceptron artificial neural network for phase retrieval[18]](/richHtml/qjglzs/2021/33/8/081004/img_1.jpg)
Fig. 1. Perceptron artificial neural network for phase retrieval[18]
![Modified Inception v3CNN model for predicting Zernike coefficients[20]](/richHtml/qjglzs/2021/33/8/081004/img_2.jpg)
Fig. 2. Modified Inception v3CNN model for predicting Zernike coefficients[20]
![Zernike coefficients predicting results of focused target[23]](/Images/icon/loading.gif){kind=icon}
Fig. 3. Zernike coefficients predicting results of focused target[23]
Fig. 4. Zernike coefficients predicting results of overexposed target[23]
Fig. 5. WFSless system architecture[26]
Fig. 6. Architecture of CNN[26]
Fig. 7. Data flow of training and predictions[29]
Fig. 8. Residual wavefront RMS with and without compensation under different turbulence levels[29]
Fig. 9. Zernike coefficients prediction results by models trained with dataset of different turbulence levels[31]
Fig. 10. Trained neural network is optimized by TensorRT to build the inference engine for implementation[35]
Fig. 11. Wavefront Net (WFNet)[36]
Fig. 12. CNN architecture[38]
Fig. 13. Standard deviation of phase before and after phase aberration revision[38]
Fig. 14. An object irrelevant wavefront sensing scheme using LSTM neural network[41]
Fig. 15. Image restoration results based on wavefront error inferred by LSTM[41]
Fig. 16. Prediction results of the next 5 frames wavefront made by LSTM[44]
Fig. 17. Reinforcement Learning(RL) of WFSless AO[47]
Fig. 18. Intensity distribution of point target with wavefront error and that after restoration by deep RL[47]
Fig. 19. PSD of different vibration frequency[51]
Fig. 20. Residual phase[51]
Fig. 21. Principle of aberration correction in high resolution optical microscopes[59]
Fig. 22. Schematic diagram of MAL-WSAO-SS-OCT system[63]
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Table 1. Accuracy of Zernike coefficients (RMS) with overexposure, defocus and scattering preprocessing[23]
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Table 2. Dataset of three different turbulence levels[31]
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Table 3. Simulation results of wavefront restoration error under different turbulence levels (NPMS: Normalized Pixel Mean Square; RMS: Root Mean Square)[32]
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Table 4. Wavefront restoration error and time consumption of experiments (NPMS: Normalized Pixel Mean Square; RMS: Root Mean Square)[32]
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Table 5. Comparison of inference time of PD-CNN with that of Xception[34]
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Table 6. Comparison of inference time with and without optimization by TensorRT[34]
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Table 7. WFSless AO simulation software
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