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首页> 外文会议>RIAO/OPTILAS 2004 >Autofocus algorithm using one-dimensional Fourier transform and Pearson correlation
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Autofocus algorithm using one-dimensional Fourier transform and Pearson correlation

机译：使用一维傅里叶变换和Pearson相关的自动聚焦算法

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摘要

A new autofocus algorithm based on one-dimensional Fourier transform and Pearson correlation for Z automatized microscope is proposed. Our goal is to determine in fast response time and accuracy, the best focused plane through an algorithm. We capture in bright and dark field several images set at different Z distances from biological organism sample. The algorithm uses the one-dimensional Fourier transform to obtain the image frequency content of a vectors pattern previously defined comparing the Pearson correlation of these frequency vectors versus the reference image frequency vector, the most out of focus image, we find the best focusing. Experimental results showed the algorithm has fast response time and accuracy in getting the best focus plane from captured images. In conclusions, the algorithm can be implemented in real time systems due fast response time, accuracy and robustness. The algorithm can be used to get focused images in bright and dark field and it can be extended to include fusion techniques to construct multifocus final images beyond of this paper.
机译：提出了一种基于一维傅里叶变换和皮尔逊相关的自动聚焦算法。我们的目标是通过算法确定快速响应时间和准确性最佳聚焦平面。我们在明亮和黑暗的领域中捕获了从生物样本到不同Z距离的几幅图像。该算法使用一维傅立叶变换来获得先前定义的矢量模式的图像频率内容，将这些频率矢量与参考图像频率矢量的皮尔逊相关性进行比较，以最不聚焦的图像为例，我们发现聚焦效果最佳。实验结果表明，该算法具有更快的响应时间和从捕获的图像中获得最佳聚焦平面的准确性。总之，由于响应时间快，准确性和鲁棒性强，该算法可以在实时系统中实现。该算法可用于获取明场和暗场中的聚焦图像，并且可以扩展为包括融合技术以构造多聚焦最终图像。

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