Auto-focus windows selection algorithm for optical microscope
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摘要: 针对全自动光学显微镜系统中，传统聚焦窗口选择方法易受图像内容分布、杂质、噪声等因素干扰的问题，提出一种根据内容像素变化量选择聚焦窗口的方法。该方法将灰度差像素数量与边缘像素数量加权作为内容像素数量，据此衡量失焦模糊状态下子块内容含量并划分聚焦窗口，减少杂质与噪声对取窗过程的影响；用降采样后图像各子块内容含量估计原图像内容分布信息，降低图像滤波、梯度计算过程的计算量；使用局部标准差与锐利边缘像素数量联合检测焦平面图像的失焦模糊区域，有效排除玻片杂质造成的焦平面误判。与传统的显微镜自动聚焦取窗方法相比，对内容丰富程度和分布状况不同的显微图像序列，该方法均能获取有效的聚焦窗口，像素梯度均值更高，所得的评价曲线局部极值极少，尖锐性好，因此该方法的成功率高，鲁棒性更强。Abstract: Aiming at the problem that traditional auto-focus windows selection algorithms are susceptible to content distribution, impurities and noise, an auto-focus windows selection method depending on variation of content pixels is proposed. The method takes a weighted average of the amount of gray-leveldifference pixels and the amount of edge pixels as the number of content pixels, then measures image content of sub-block in blurred state and divides focusing windows by the variation of content pixels. In the meantime, the proposed method estimates content distribution information of original image using image content of each sub-block in downsampling image. Besides, the proposed method detects out-of-blurred areas using combined detection of local standard deviation and sharp edge pixels, and it effectively excludes the misjudgments of focal plane caused by the impurities on coverslips. Compared with the traditional auto-focus windows selection algorithms, the proposed method could obtain effective focusing windows for each microscopic sequence varying in richness and distribution of the image content, the corresponding mean gradient of pixels is higher, and the acquired focus measure curve has fewer local extreme values and sharp performance, therefore, the proposed method has more advantages in success ratio and robustness.
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Keywords:
- microscope /
- focused windows /
- noise /
- impurities on glass coverslips
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