An automatic focusing algorithm based on U-Net for target location in multiple depth-of-field scene

Liang Luyao; Zhao Xiaoyun; Zhao Jinquan

doi:10.11884/HPLPB202234.220086

Volume 34 Issue 12

Nov. 2022

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Liang Luyao, Zhao Xiaoyun, Zhao Jinquan. An automatic focusing algorithm based on U-Net for target location in multiple depth-of-field scene[J]. High Power Laser and Particle Beams, 2022, 34: 129001. doi: 10.11884/HPLPB202234.220086

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Liang Luyao, Zhao Xiaoyun, Zhao Jinquan. An automatic focusing algorithm based on U-Net for target location in multiple depth-of-field scene[J]. High Power Laser and Particle Beams, 2022, 34: 129001. doi: 10.11884/HPLPB202234.220086

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An automatic focusing algorithm based on U-Net for target location in multiple depth-of-field scene

doi: 10.11884/HPLPB202234.220086

1.
College of Mathmatics and Physics, Chengdu University of Technology, Chengdu 610000, China
2.
The college of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu 610000, China
3.
School of Geophysics, Chengdu University of Technology, Chengdu 610000, China

Received Date: 2022-03-28
Accepted Date: 2022-10-09
Rev Recd Date: 2022-10-07

Available Online: 2022-11-02

Publish Date: 2022-11-02

Abstract

Abstract

Evaluation function of automatic focusing system is the key to evaluate image quality. In multi-depth-of-field scenarios, when the target is located in the center of the image, the sensitivity of the traditional focusing evaluation curve is low; when the target deviates from the center, the focus evaluation function curve is prone to local maximum, which affects the accuracy of the automatic focusing system. In view of these two situations, this paper proposes a method based on U-Net neural network and sets the corresponding window and evaluation function. When the object is located in the center of the image, a new focusing evaluation function, SMD-Roberts function, is proposed. When the target is not in the center of the image, the corresponding window is set for the image and the SML evaluation function is selected to evaluate the image quality. Experimental results show that , compared with traditional focused evaluation function and central window method, this method can effectively solve the problem that the focus evaluation function is not accurate in judging the clearest position of the object and the double peak of the focusing evaluation function curve in multi-depth-of-field scenes and obviously improve the unbiasedness, unimodal and sensitivity of the focused evaluation function. This method has strong universality and is more suitable for focused evaluation system.
- automatic focus,
- evaluation function,
- neural network,
- image segmentation,
- gray gradient

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References(30)

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