显微视觉快速自动调焦方法及实验

分析了显微视觉与计算机宏观视觉的特性,得出显微视觉下模糊效果是由几何光学和波动光学两部分造成的结论,并用标定实验验证了显微视觉下扩散参数与物距呈线性关系这一假设。研究了现有基于聚焦的自动调焦DFD(Depth from Focus)方法,提出了显微视觉下一种新型的基于离焦(Depth from Defocus)的快速自动调焦算法,该算法只要给定两幅模糊图像,就可直接计算出目标聚焦平面位置。实验结果显示,该方法的聚焦速度比传统DFF方法(本文选择SML法)快2～4倍。改进了的DFD算法提高了自动调焦性能,增强了显微光学鲁棒性,调焦精度较高,且具有较好的实用性。

Fast autofocus method for microscopic computer vision

The characteristics of macro and micro computer visions were analyzed,obtained results demonstrate that micro-vision blurred effects are caused by both geometrical and wave optics. A calibration experiment was carried out,which verifies the assumption of the linear relationship between spread parameter and step number. On the basis of above research,the existing autofocus technology based Depth from Focus(DFF) was introduced and a fast autofocus algorithm based Depth from Defocus(DFD) was presented in micro computer vision. The algorithm can calculate the focusing position of a target directly by giving two frames of blurred images. Experimental results indicate that the focusing speed obtained by proposed algorithm is 2-4 times faster than that gotten by traditional method(Sum Modified Laplacisn Method,SLM). It is shown that the proposed method has better focusing performance and robustness and can be used in the autofocus for micro computer vision systems.