Target-independent dynamic wavefront sensing method based on distorted grating and deep learning

Xinlan Ge; Licheng Zhu; Zeyu Gao; Ning Wang; Wang Zhao; Hongwei Ye; Shuai Wang; Ping Yang

doi:doi:10.3788/COL202321.060101

Chinese Optics Letters, 2023, 21 (6): 060101, Published Online: Jun. 9, 2023

Target-independent dynamic wavefront sensing method based on distorted grating and deep learning

Xinlan Ge ^1,2,3Licheng Zhu ^1,2,*Zeyu Gao ^1,2Ning Wang ^1,2Wang Zhao ^1,2Hongwei Ye ^1,2Shuai Wang ^1,2Ping Yang ^1,2,**

Author Affiliations

¹ Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, China

² Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China

³ School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

Figures & Tables

Fig. 1. (a) Schematic of the distorted grating and (b) geometric relationship of the ±1st diffraction order imaging positions.

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Fig. 2. Schematic of target-independent wavefront sensing.

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Fig. 3. Structure of the AM-EffNet for target-independent wavefront sensing.

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Fig. 4. (a) Time domain feature. The red boxes in (b) are the enumerated power features, and the blue boxes in (c) are the sharpness features. We can see that the normalized fine feature in (d) includes the information of the sharpness and the power features with data distributed between 0 and 1. (e) Comparison of loss for different features in training.

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Fig. 5. Residual wavefront at different defocus amount. The letters A, B, C, D, and E in the abscissa express gratings with different defocusing degrees, and the numbers 1, 2, and 3 represent three different degrees of atmospheric turbulence in the range of 0–0.5λ, 0.5λ–1.0λ, and 1.0λ–1.5λ of the original wavefront RMS, respectively. The red circles represent outliers.

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Fig. 6. A group of wavefront sensing results of targets in five different scenarios with our method. The RMSE/Truth represents the ratio of the residual wavefront to the true wavefront.

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Table1. Testing Results Based on Our Method

Target type	RMS (λ)	Time (ms)	SSIM	RMSE/Truth
Point source	0.040	1.96	0.952	5.5%
Resolution chart	0.046	1.99	0.949	5.6%
Remote sensing	0.035	2.05	0.952	5.3%
Bird	0.038	2.04	0.948	5.4%
Nebula	0.051	1.96	0.943	6.1%

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Xinlan Ge, Licheng Zhu, Zeyu Gao, Ning Wang, Wang Zhao, Hongwei Ye, Shuai Wang, Ping Yang. Target-independent dynamic wavefront sensing method based on distorted grating and deep learning[J]. Chinese Optics Letters, 2023, 21(6): 060101.

Target-independent dynamic wavefront sensing method based on distorted grating and deep learning

Fig. 1. (a) Schematic of the distorted grating and (b) geometric relationship of the ±1st diffraction order imaging positions.

Fig. 2. Schematic of target-independent wavefront sensing.

Fig. 3. Structure of the AM-EffNet for target-independent wavefront sensing.

Fig. 6. A group of wavefront sensing results of targets in five different scenarios with our method. The RMSE/Truth represents the ratio of the residual wavefront to the true wavefront.

Table1. Testing Results Based on Our Method

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Target-independent dynamic wavefront sensing method based on distorted grating and deep learning

Fig. 1. (a) Schematic of the distorted grating and (b) geometric relationship of the ±1st diffraction order imaging positions.

Fig. 2. Schematic of target-independent wavefront sensing.

Fig. 3. Structure of the AM-EffNet for target-independent wavefront sensing.

Fig. 6. A group of wavefront sensing results of targets in five different scenarios with our method. The RMSE/Truth represents the ratio of the residual wavefront to the true wavefront.

Table1. Testing Results Based on Our Method

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