Improved machine learning approach for wavefront sensing
文献类型：期刊论文
| 作者 | Guo, Hongyang1,3,4; Xu, Yangjie1,3,4; Li, Qing1,2,3,4; Du, Shengping1,4; He, Dong1,4; Wang, Qiang1,4; Huang, Yongmei1,4 |
| 刊名 | Sensors (Switzerland)
 |
| 出版日期 | 2019-08-20 |
| 卷号 | 19期号:16 |
| 关键词 | adaptive optics machine learning convolutional neural network deconvolution |
| ISSN号 | 1424-8220 |
| DOI | 10.3390/s19163533 |
| 文献子类 | 期刊论文 |
| 英文摘要 | In the adaptive optics (AO) system, to improve the e_ectiveness and accuracy of wavefront sensing-less technology, a phase-based sensing approach using machine learning is proposed. In contrast to the traditional gradient-based optimization methods, the model we designed is based on an improved convolutional neural network. Specifically, the deconvolution layer, which reconstructs unknown input by measuring output, is introduced to represent the phase maps of the point spread functions at the in focus and defocus planes. The improved convolutional neural network is utilized to establish the nonlinear mapping between the input point spread functions and the corresponding phase maps of the optical system. Once well trained, the model can directly output the aberration map of the optical system with good precision. Adequate simulations and experiments are introduced to demonstrate the accuracy and real-time performance of the proposed method. The simulations show that even when atmospheric conditions D/r0 = 20, the detection root-mean-square of wavefront error of the proposed method is 0.1307 λ, which has a better accuracy than existing neural networks. When D/r0 = 15 and 10, the root-mean-square error is respectively 0.0909 λ and 0.0718 λ. It has certain applicative value in the case of medium and weak turbulence. The root-mean-square error of experiment results with D/r0 = 20 is 0.1304 λ, proving the correctness of simulations. Moreover, this method only needs 12 ms to accomplish the calculation and it has broad prospects for real-time wavefront sensing. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. |
| WOS关键词 | PHASE-RETRIEVAL ALGORITHMS ; ADAPTIVE OPTICS ; SENSOR |
| WOS研究方向 | Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000484407200090 |
| 出版者 | MDPI AG, Postfach, Basel, CH-4005, Switzerland |
| 源URL | [http://ir.ioe.ac.cn/handle/181551/9802]  |
| 专题 | 光电技术研究所_光电工程总体研究室（一室） |
| 作者单位 | 1.The Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu; 610209, China; 2.School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, No. 4 Section 2 North Jianshe Road, Chengdu; 610054, China 3.University of Chinese Academy of Sciences, Beijing; 100049, China; 4.Key Laboratory of Optical Engineering, Chinese Academy of Sciences, No.1 Guangdian Road, Chengdu; 610209, China; |
| 推荐引用方式 GB/T 7714 | Guo, Hongyang,Xu, Yangjie,Li, Qing,et al. Improved machine learning approach for wavefront sensing[J]. Sensors (Switzerland),2019,19(16). |
| APA | Guo, Hongyang.,Xu, Yangjie.,Li, Qing.,Du, Shengping.,He, Dong.,...&Huang, Yongmei.(2019).Improved machine learning approach for wavefront sensing.Sensors (Switzerland),19(16). |
| MLA | Guo, Hongyang,et al."Improved machine learning approach for wavefront sensing".Sensors (Switzerland) 19.16(2019). |
入库方式： OAI收割
来源：光电技术研究所
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