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Proceedings Paper

Auto-focus algorithm based on improved SML evaluation function
Author(s): Xiaoyu Ma; Qiaoling Li
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Paper Abstract

The traditional spatial domain sharpness evaluation functions usually have a larger amount of calculation, and the calculation time is relatively longer. Besides, its anti-noise ability is weak, and it is easy to be disturbed by the background factors in the image. The above problems will have an impact on the real-time, sensitivity and reliability of the auto-focus system. In order to overcome these shortcomings, an improved SML sharpness evaluation function combined with threshold is proposed in this paper. This algorithm improve the SML function firstly, and make full use of the edge information of the image. Then a threshold is introduced to distinguish the edge points from non-edge points. So it can not only highlight the edge information while restraining the noise and the flat area in the background of the image, but also can reduce the calculation amount of the evaluation function and improve the real-time performance of the auto-focusing system. Finally verifies the effect of the improved evaluation function based on the simulation experiments. The results show that the algorithm proposed in this paper has better sensitivity and anti-noise ability, and can evaluate the sharpness of defocused images accurately and steadily.

Paper Details

Date Published: 18 December 2019
PDF: 6 pages
Proc. SPIE 11338, AOPC 2019: Optical Sensing and Imaging Technology, 113381Z (18 December 2019); doi: 10.1117/12.2545624
Show Author Affiliations
Xiaoyu Ma, Xi'an Institute of Optics and Precision Mechanics (China)
Univ. of Chinese Academy of Sciences (China)
Qiaoling Li, Xi'an Institute of Optics and Precision Mechanics (China)

Published in SPIE Proceedings Vol. 11338:
AOPC 2019: Optical Sensing and Imaging Technology
John E. Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

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