基于红外自动聚焦过程的最优函数选取

Optimal Function Selection Based on Infrared Auto-Focusing Processes

  • 摘要: 与可见光自动聚焦系统相比,红外自动聚焦系统的核心问题在于因红外探测器特殊的成像原理,聚焦过程被分为由远及近聚焦和由近及远聚焦。针对这两个过程所使用的自动聚焦函数,在分析各自聚焦函数曲线特征的基础上,运用灵敏度、陡峭区宽度、陡峭度、平缓区波动量和时间5个针对性的评价指标,对常用的13个典型的清晰度评价函数进行定量分析,提出适合两个聚焦过程的最优函数。结论表明:在由近及远的聚焦过程中,FLaplace可作为该过程的最优函数;而由远及近的聚焦过程中,FLaplaceFSML可作为最优函数的选取。

     

    Abstract: Unlike the visible light auto-focusing system, the infrared auto-focusing system is divided into far-to-near focusing and near-to-far focusing owing to the special imaging principle of the infrared detector. The auto-focusing functions in the two processes are based on the analysis of the characteristics of the respective focusing function curves. To this end, five targeted evaluation indexes are used: sensitivity, the width of the steep part of the focusing curve, steepness, variance of the flat part of the focusing curve, and time. The 13 typical sharpness evaluation functions that are commonly used in quantitative analysis are conducted, and an optimal function suitable for the two focusing processes is proposed. The results show that FLaplace can be used as the optimal function in the focusing process from near to far, and FLaplace and FSML can be used as the optimal function in focusing from near to far.

     

/

返回文章
返回