Deep learning phase inversion technique for single frame image based on Walsh function modulation

Liu Qi; Du Yinglei; Xiang Rujian; Li Guohui; Zhang Qiushi; Xiang Zhenjiao; Wu Jing; Yue Xian; Bao Anchao; You Jiang

doi:10.11884/HPLPB202436.240048

Article Contents

Article Navigation > High Power Laser and Particle Beams > 2024 > Corrected proofs

Liu Qi, Du Yinglei, Xiang Rujian, et al. Deep learning phase inversion technique for single frame image based on Walsh function modulation[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202436.240048

Citation:

Liu Qi, Du Yinglei, Xiang Rujian, et al. Deep learning phase inversion technique for single frame image based on Walsh function modulation[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202436.240048

Citation:

PDF( 1852 KB)

Deep learning phase inversion technique for single frame image based on Walsh function modulation

doi: 10.11884/HPLPB202436.240048

Liu Qi^{1, 2
,},
Du Yinglei^{1
,
,},
Xiang Rujian¹,
Li Guohui¹,
Zhang Qiushi¹,
Xiang Zhenjiao¹,
Wu Jing¹,
Yue Xian¹,
Bao Anchao¹,
You Jiang^{1, 2}

1.
Institute of Applied Electronics, CAEP, Mianyang 621900, China
2.
Graduate School of China Academy of Engineering Physics, Beijing 100088, China

Received Date: 2024-02-01
Accepted Date: 2024-03-28
Rev Recd Date: 2024-03-28

Available Online: 2024-04-15

Abstract

Abstract

The far-field phase inversion exhibits degeneracy states, leading to the problem of encountering multiple solutions when recovering the wavefront. In comparison to traditional iterative algorithms, the combination of phase modulation and deep learning in the phase inversion method not only significantly reduces computational complexity but also effectively solves multi-solution problems. This method possesses strong real-time capabilities and a simple structure, showcasing its unique advantages. In this paper, different Walsh functions are used to modulate the phase, and a deep learning approach is taken to train a convolutional neural network to obtain the 4th-30th order Zernike coefficients from the modulated single-frame far-field intensity maps so as to recover the original wavefront, which solves the problem of multiple solutions of phase inversion. For the residual wavefront of the turbulent aberration of 3-15 cm atmospheric coherence length, the ratio of its RMS to the RMS of the original wavefront can reach 7.8%. In addition, this paper also deeply investigates the effects of various factors such as Zernike order, random noise, occlusion, and intensity map resolution on the wavefront recovery accuracy. The results show that this deep learning-based phase inversion method exhibits good robustness in complex environment.
- phase inversion,
- Zernike coefficient,
- phase modulation,
- deep learning,
- Walsh function

FullText(HTML)

References(16)

References

[1]	Gerchberg R W, Saxton W O. A practical algorithm for the determination of phase from image and diffraction plane pictures[J]. Optik, 1972, 35(2): 237-246.
[2]	Gonsalves R A. Phase retrieval and diversity in adaptive optics[J]. Optical Engineering, 1982, 21: 215829.
[3]	张杏云, 罗芳琳, 李楠, 等. 相位差波前探测与图像重建[J]. 强激光与粒子束, 2021, 33:081010 doi: 10.11884/HPLPB202133.210203 Zhang Xingyun, Luo Fanglin, Li Nan, et al. Phase diversity wavefront sensing and image reconstruction[J]. High Power Laser and Particle Beams, 2021, 33: 081010 doi: 10.11884/HPLPB202133.210203
[4]	张全. 相位差算法的并行化分析与实现[D]. 成都: 电子科技大学, 2015: 66-98 Zhang Quan. Parallel analysis and implementation of phase diversity[D]. Chengdu: University of Electronic Science and Technology of China, 2015: 66-98
[5]	孔庆峰. 基于单帧焦面图像的波前相位反演方法研究[D]. 成都: 电子科技大学, 2019: 35-44 Kong Qingfeng. Research on wavefront phase retrieval method based on single frame focal plane image[D]. Chengdu: University of Electronic Science and Technology of China, 2019: 35-44
[6]	孔庆峰, 王帅, 杨平, 等. 基于Walsh函数调制的单帧远场波前反演方法[J]. 光电工程, 2020, 47:190323 Kong Qingfeng, Wang Shuai, Yang Ping, et al. Single-frame far-field wavefront retrieval method based on Walsh function modulation[J]. Opto-electronic Engineering, 2020, 47: 190323
[7]	Xin Qi, Ju Guohao, Zhang Chunyue, et al. Object-independent image-based wavefront sensing approach using phase diversity images and deep learning[J]. Optics Express, 2019, 27(18): 26102-26119. doi: 10.1364/OE.27.026102
[8]	张阳, 何宇龙, 宁禹, 等. 远场光斑反演波前相位的深度学习方法[J]. 红外与激光工程, 2021, 50:20200363 doi: 10.3788/IRLA20200363 Zhang Yang, He Yulong, Ning Yu, et al. Method of inverting wavefront phase from far-field spot based on deep learning[J]. Infrared and Laser Engineering, 2021, 50: 20200363 doi: 10.3788/IRLA20200363
[9]	史宗佳, 向振佼, 杜应磊, 等. 基于远场信息和卷积神经网络的波前重构方法[J]. 强激光与粒子束, 2021, 33:081011 doi: 10.11884/HPLPB202133.210040 Shi Zongjia, Xiang Zhenjiao, Du Yinglei, et al. Wavefront reconstruction method based on far-field information and convolutional neural network[J]. High Power Laser and Particle Beams, 2021, 33: 081011 doi: 10.11884/HPLPB202133.210040
[10]	周静, 张晓芳, 赵延庚. 一种基于图像融合和卷积神经网络的相位恢复方法[J]. 物理学报, 2021, 70:054201 doi: 10.7498/aps.70.20201362 Zhou Jing, Zhang Xiaofang, Zhao Yangeng. Phase retrieval wavefront sensing based on image fusion and convolutional neural network[J]. Acta Physica Sinica, 2021, 70: 054201 doi: 10.7498/aps.70.20201362
[11]	邱学晶. 基于四象限离散相位调制的单帧图像深度学习相位反演算法研究[D]. 成都: 中国科学院大学, 2021: 28-30 Qiu Xuejing. Research on single frame deep learning phase retrieval method based on four-quadrant discrete phase modulation[D]. Chengdu: University of Chinese Academy of Sciences, 2021: 28-30
[12]	McGlamery B L. Computer simulation studies of compensation of turbulence degraded images[J]. Journal of the Optical Society of America, 1976, 66(2): 225-233.
[13]	杨海波, 许宏. 基于功率谱反演法的大气湍流相位屏数值模拟[J]. 光电技术应用, 2019, 34(4):73-76 Yang Haibo, Xu Hong. Numerical simulation of atmospheric turbulence phase screen based on power spectrum inversion method[J]. Electro-Optic Technology Application, 2019, 34(4): 73-76
[14]	Walsh J L. A closed set of normal orthogonal functions[J]. American Journal of Mathematics, 1923, 45(1): 5-24. doi: 10.2307/2387224
[15]	Wang S, Yang P, Ao M W, et al. Wavefront sensing by means of binary intensity modulation[J]. Applied Optics, 2014, 53(35): 8342-8349. doi: 10.1364/AO.53.008342
[16]	He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016: 770-778.