.png){kind=icon}
返回
Deep-learning-based deflectometry for freeform surface measurement
.png){kind=icon}
PRE
AI
DOI：10.1364/OL.447006.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We propose a deep-learning based deflectometric method for freeform surface measurement, in which a deep neural network is devised for freeform surface reconstruction. Full-scale skip connections are adopted in the network architecture to extract and incorporate multi-scale feature maps from different layers, enabling the accuracy and robustness of the testing system to be greatly enhanced. The feasibility of the proposed method is numerically and experimentally validated, and its excellent performance in terms of accuracy and robustness is also demonstrated. The proposed method provides a feasible way to achieve the general measurement of freeform surfaces while minimizing the measurement errors due to noise and system geometry calibration. (C) 2021 Optica Publishing Group
Keyword:
WAVE-FRONT
REFLECTIVE SURFACE
DESIGN
期刊
IF:
3.3
论文数:
4.0W
被引数:
7.6W
机构
引用论文
Simulation and validation of a prototype swing arm profilometer for measuring extremely large telescope mirror-segments.png)
OPTICS EXPRESS
OAAI
OPTICS EXPRESSIF3.3
OAAI分享收藏Intensity-enhanced deep network wavefront reconstruction in Shack Hartmann sensorsOPTICS LETTERS
OPTICS LETTERSIF3.3
PREAI
分享收藏Precise light control in highly tilted geometry by freeform illumination opticsOPTICS LETTERS
OPTICS LETTERSIF3.3
PREAI
分享收藏Computer-aided high-accuracy testing of reflective surface with reverse Hartmann testOPTICS EXPRESS
OAAI
OPTICS EXPRESSIF3.3
OAAI分享收藏Accurate calibration of geometrical error in reflective surface testing based on reverse Hartmann testOPTICS EXPRESSOAAI
OPTICS EXPRESSIF3.3
OAAI分享收藏
查看更多内容