High Power Laser and Particle Beams, Volume. 33, Issue 8, 081011(2021)
Wavefront reconstruction method based on far-field information and convolutional neural network
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Fig. 5. The L1 loss change process of the four ResNet models in training is shown in (a) the training set and (b) the verification set
Fig. 7. Wavefront reconstruction results of a sample in the test set
Fig. 8. Scatter plot of PV and RMS of original wavefront and wavefront residuals of test set samples
Fig. 9. Ratio of PV and RMS of sample wavefront residuals to original wavefront of test set
Table 1. Wavefront reconstruction results of turbulence with different intensities
View tableView in ArticleTable 1. Wavefront reconstruction results of turbulence with different intensities
R0 far field image size/ pixel L1 error normalized coefficient RMSE PV of the test set samples’ original wavefront/μm RMS of the test set samples’ original wavefront/μm PV of reconstructed wavefront residuals/μm RMS of reconstructed wavefront residuals/μm residual PV to original wavefront ratio (90% of sample)/% residual RMS to original wavefront ratio (90% of sample)/% 1 140×140 0.0040 0.0051 2.67±1.63 0.54±0.37 0.12±0.07 0.02±0.01 6 5 0.5 200×200 0.0204 0.0266 5.06±2.76 1.0±0.55 1.14±0.75 0.20±0.13 30 27
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Zongjia Shi, Zhenjiao Xiang, Yinglei Du, Min Wan, Jingliang Gu, Guohui Li, Rujian Xiang, Jiang You, Jing Wu, Honglai Xu. Wavefront reconstruction method based on far-field information and convolutional neural network[J]. High Power Laser and Particle Beams, 2021, 33(8): 081011
Paper Information
Category: Laser Atmosphere Propagation
Received: Feb. 4, 2021
Accepted: --
Published Online: Sep. 3, 2021
The Author Email: Yinglei Du (boyduyinglei@163.com)
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