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Autofocus algorithm using optimized Laplace evaluation function and enhanced mountain climbing search algorithm
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AI
DOI：10.1007/s11042-022-12191-w.png)
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Abstract
En 中文
In the field of digital imaging systems, autofocus plays increasingly a vital role as a key technology. Autofocus poses a great challenge due to nosiy background and slow focusing speed. This paper presents a new focusing algorithm based on improved Laplacian operator and mountain-climb search algorithm. The clear image after focusing is more different in gray scale than the image without focusing, an image definition evaluation function combining local variance and Laplacian operator is proposed. Learning from the advantages of two-stage recognition in deep learning image recognition, an two-stage search algorithm based on mountain-climb search is designed to better fit the focusing curve near the extreme value of focusing evaluation function, improved mountain-climb search algorithm is divided into rough focusing and fine focusing. The method of rough focusing is used to determine a small focus area, and then fine focusing based on function approximation can greatly improve the efficiency of focus position.The experimental results indicate that this algorithm in this paper is superior to the traditional algorithm in time and accuracy, and the time of the autofocus is reduced by 76%.
Keywords:
Microscope imaging
Autofocus
Mountain climbing search
Journal
IF:
3
Papers:
1.9W
Citations:
3.2W