Applications of hybrid search strategy in microscope autofocus

Minshan Jiang; Nannan Zhang; Xuedian Zhang; Jia Gu; Xue Li; Feng Li

doi:10.3969/j.issn.1003-501X.2017.07.004

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Minshan Jiang, Nannan Zhang, Xuedian Zhang, et al. Applications of hybrid search strategy in microscope autofocus[J]. Opto-Electronic Engineering, 2017, 44(7): 685-694. doi: 10.3969/j.issn.1003-501X.2017.07.004

Citation:

Minshan Jiang, Nannan Zhang, Xuedian Zhang, et al. Applications of hybrid search strategy in microscope autofocus[J]. Opto-Electronic Engineering, 2017, 44(7): 685-694. doi: 10.3969/j.issn.1003-501X.2017.07.004

Applications of hybrid search strategy in microscope autofocus

Institute of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

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Corresponding author: Feng Li, E-mail: lifenggold@163.com

Received Date 28 April 2017

Revised Date 24 June 2017

Published Date 15 July 2017

Abstract

Abstract

For autofocus system of the microscope, this paper presents a hybrid search algorithm combining the mountain-climb search strategy with the approximation function strategy. In this algorithm, the mountain-climb search strategy adopts the two-stage algorithm of rough and fine focusing stage. In the rough focusing stage, the gray variance function is used to approach the focusing position quickly. In the fine focusing stage, the Laplacian function is used to locate the focusing position accurately. The algorithm narrows the focus interval by comparing three pictures and the approximation function strategy is used to fit the best focus in this range. This method greatly reduces the number of images required for autofocus and greatly improves the search accuracy. The experimental results indicate that this algorithm can make the search accuracy better than 1 μm.
- autofocus /
- search algorithm /
- mountain-climb search strategy /
- approximation function strategy /
- hybrid search strategy

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References

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Overview

Overview

Abstract: Auto-focusing is one of the key technologies in the area of robot vision, digital imaging systems and precision optical instrument. With the continuous development of science and technology and improved application demands, it is more and more urgent to develop an auto-focusing with high precision, fast speed and good stability. While the existing auto-focusing techniques can’t meet the above requirements, a further study on auto-focusing makes a very important practical significance. The depth from defocus method and the depth from focus method are two typical passive auto-focusing methods of autofocus method based on digital image processing. The depth from defocus method is popularly used in depth estimation and scene reconstruction, which can measure the position of samples by just a few images. Therefore, the efficiency of the method is high. However, the accuracy of the depth from defocus method is relatively low because the small number of images is collected by the method. The depth from focus methods are based on the fact that the image formed by an optical system is focused at a particular distance whereas objects at other distances are blurred or defocused. Very high accuracy can be achieved by depth from focus methods. In order to achieve efficient autofocusing, several commonly used search algorithms are studied, and a new low-computational search algorithm is presented, which combines the mountain-climb search strategy with the approximation function strategy to realize the hybrid search algorithm accurate and efficient autofocus. In this algorithm, the mountain-climb search strategy adopts the two-stage algorithm of rough and fine focusing stage. In the rough focusing stage, the large step distance takes into account the fastness of the algorithm, and the gray variance function is used to approach the focusing position quickly. In the fine focusing stage, the small step distance takes into account the sensitivity of the algorithm and the Laplacian function is used to locate the focusing position accurately. The algorithm narrows the focus interval by comparing three pictures and in the range uses the approximation function strategy to fit the best focus position. This method makes greatly improve the search accuracy. The experimental results indicate that this the algorithm can make the search accuracy better than 1 μm. And the method only needs to capture 17 pictures, reducing the number of image acquisition and evaluation. As a result, the time of the autofocus system is shortened and the search efficiency of the algorithm is improved.