.png){kind=icon}
返回
Deep learning assisted plenoptic wavefront sensor for direct wavefront detection
.png){kind=icon}
OA
AI
DOI：10.1364/OE.478239.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Traditional plenoptic wavefront sensors (PWFS) suffer from the obvious step change of the slope response, leading to poor wavefront detection performance. In order to solve this problem, in this paper, a deep learning model is proposed to restore phase maps directly from slope measurements of PWFS. Numerical simulations are employed to demonstrate our approach, and the statistical residual wavefront root mean square error (RMSE) of our method is 0.0810 +/- 0.0258., which is much superior to those of modal algorithm (0.2511 +/- 0.0587.) and zonal approach (0.3584 +/- 0.0487.). The internal driving force of PWFS-ResUnet is investigated, and the slope response differences between sub-apertures and directions are considered as a probably key role to help our model to accurately restore the phase map. Additionally, the robustness of our model to turbulence strength and signal-to-noise ratio (SNR) level is also tested. The proposed method provides a new direction to solve the nonlinear problem of traditional PWFS.
Keyword:
FOCAL-PLANE
PHASE RETRIEVAL
NEURAL-NETWORKS
SUBDIVISION
期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
机构
引用论文
Clinical predictors for diabetes screening in the first year postpartum after gestational diabetes
OAAI
OAAI分享收藏Learning-based Shack-Hartmann wavefront sensor for high-order aberration detection.png)
OPTICS EXPRESSOAAI
OPTICS EXPRESSIF3.3
OAAI分享收藏Description of new members of the family Cylindroleberididae (Ostracoda: Cylindroleberidoidea) from the Southern OceanZootaxa
ZootaxaIF0
PREAI
分享收藏Conformal convolutional neural network (CCNN) for single-shot sensorless wavefront sensing用于单次无传感器波前传感的共形卷积神经网络 (CCNN)OPTICS EXPRESSOAAI
OPTICS EXPRESSIF3.3
OAAI分享收藏分享收藏Intensity-enhanced deep network wavefront reconstruction in Shack Hartmann sensorsOPTICS LETTERS
OPTICS LETTERSIF3.3
PREAI
分享收藏
查看更多内容