Piston Sensing for Golay-6 Sparse Aperture System with Double-Defocused Sharpness Metrics via ResNet-34
Senmiao Wang,
Quanying Wu,
Junliu Fan,
Baohua Chen,
Xiaoyi Chen,
Lei Chen,
Donghui Shen,
Lidong Yin
Affiliations
Senmiao Wang
Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application, School of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou 215009, China
Quanying Wu
Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application, School of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou 215009, China
Junliu Fan
Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application, School of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou 215009, China
Baohua Chen
Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application, School of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou 215009, China
Xiaoyi Chen
Suzhou Mason Optical Co., Ltd., Suzhou 215028, China
Lei Chen
Currently School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Donghui Shen
Suzhou Dechuang Measurement & Control Technology Co., Ltd. of Graduate Workstation in Jiangsu Province, Suzhou 215128, China
Lidong Yin
Zhangjiagang Optical Instrument Co., Ltd. of Graduate Workstation in Jiangsu Province, Suzhou 215006, China
In pursuit of high imaging quality, optical sparse aperture systems must correct piston errors quickly within a small range. In this paper, we modified the existing deep-learning piston detection method for the Golay-6 array, by using a more powerful single convolutional neural network based on ResNet-34 for feature extraction; another fully connected layer was added, on the basis of this network, to obtain the best results. The Double-defocused Sharpness Metric (DSM) was selected first, as a feature vector to enhance the model performance; the average RMSE of the five sub-apertures for valid detection in our study was only 0.015λ (9 nm). This modified method has higher detecting precision, and requires fewer training datasets with less training time. Compared to the conventional approach, this technique is more suitable for the piston sensing of complex configurations.
