Selecting the optimal focus measure for autofocusing and depth-from-focus
Selecting the optimal focus measure for autofocusing and depth-from-focus
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DOI：
10.1109/34.709612
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发表时间：
1998-08-01
影响因子：
23.6
通讯作者：
Tyan, JK
中科院分区：
文献类型：
--
作者：
Subbarao, M;Tyan, JK
描述了一种用于在被动自动聚焦和焦深应用中从给定的一组聚焦度量中选择相对于灰度级噪声的最佳聚焦度量的方法。该方法是基于两个新的度量，已被定义为估计不同的焦点措施的噪声敏感性。第一个度量-自动聚焦不确定性度量（AUM）-在理解灰度级噪声与用于自动聚焦的透镜位置中的所得误差之间的关系方面是有用的。第二个度量自动聚焦均方根误差（ARMS误差）是与AUM密切相关的改进度量。AUM和ARMS误差度量基于聚焦测量的理论噪声敏感性分析，并且它们通过单调表达式相关。理论分析结果得到了实际和仿真实验的验证。对于给定的相机，最佳准确的聚焦测量可以根据它们的聚焦图像从一个对象到另一个对象而改变。因此，从给定集合中选择最佳聚焦度量涉及计算该集合中的所有聚焦度量。
A method is described for selecting the optimal focus measure with respect to gray-level noise from a given set of focus measures in passive autofocusing and depth-from-focus applications. The method is based on two new metrics that have been defined for estimating the noise-sensitivity of different focus measures. The first metric-the Autofocusing Uncertainty Measure (AUM)-is useful in understanding the relation between gray-level noise and the resulting error in lens position for autofocusing. The second metric Autofocusing Root-Mean-Square Error(ARMS error)-is an improved metric closely related to AUM. AUM and ARMS error metrics are based on a theoretical noise sensitivity analysis of focus measures, and they are related by a monotonic expression. The theoretical results are validated by actual and simulation experiments. For a given camera, the optimally accurate focus measure may change from one object tb the other depending on their focused images. Therefore, selecting the optimal focus measure from a given set involves computing all focus measures in the set.