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Large-Dynamic-Range Ocular Aberration Measurement Based on Deep Learning with a Shack-Hartmann Wavefront Sensor.
Zhang, Haobo; Zhao, Junlei; Chen, Hao; Zhang, Zitao; Yin, Chun; Wang, Shengqian.
Afiliação
  • Zhang H; National Laboratory on Adaptive Optics, Chengdu 610209, China.
  • Zhao J; School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • Chen H; Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China.
  • Zhang Z; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Yin C; National Laboratory on Adaptive Optics, Chengdu 610209, China.
  • Wang S; Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China.
Sensors (Basel) ; 24(9)2024 Apr 25.
Article em En | MEDLINE | ID: mdl-38732834
ABSTRACT
The Shack-Hartmann wavefront sensor (SHWFS) is widely utilized for ocular aberration measurement. However, large ocular aberrations caused by individual differences can easily make the spot move out of the range of the corresponding sub-aperture in SHWFS, rendering the traditional centroiding method ineffective. This study applied a novel convolutional neural network (CNN) model to wavefront sensing for large dynamic ocular aberration measurement. The simulation results demonstrate that, compared to the modal method, the dynamic range of our method for main low-order aberrations in ocular system is increased by 1.86 to 43.88 times in variety. Meanwhile, the proposed method also has the best measurement accuracy, and the statistical root mean square (RMS) of the residual wavefronts is 0.0082 ± 0.0185 λ (mean ± standard deviation). The proposed method generally has a higher accuracy while having a similar or even better dynamic range as compared to traditional large-dynamic schemes. On the other hand, compared with recently developed deep learning methods, the proposed method has a much larger dynamic range and better measurement accuracy.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article