计算机应用 ›› 2015, Vol. 35 ›› Issue (10): 2969-2973.DOI: 10.11772/j.issn.1001-9081.2015.10.2969

• 虚拟现实与数字媒体 • 上一篇    下一篇

基于离焦量差异定性分析的自动对焦方法

林忠, 黄陈蓉, 卢阿丽   

  1. 南京工程学院 计算机工程学院, 南京 211167
  • 收稿日期:2015-05-06 修回日期:2015-07-08 出版日期:2015-10-10 发布日期:2015-10-14
  • 通讯作者: 林忠(1981-),男,浙江舟山人,讲师,硕士,主要研究方向:机器视觉、光学成像、图像处理,linz@njit.edu.cn
  • 作者简介:黄陈蓉(1963-),女,江西南昌人,教授,博士,主要研究方向:图像处理、虚拟现实与仿真;卢阿丽(1982-),女,江苏南通人,讲师,博士,主要研究方向:计算机视觉、机器学习、模式识别。
  • 基金资助:
    国家自然科学基金资助项目(61403188);南京工程学院校级科研基金资助项目(QKJB201305,YKJ201324,CKJA201306,QKJA201306)。

Autofocus method based on blur difference qualitative analysis

LIN Zhong, HUANG Chenrong, LU Ali   

  1. College of Computer Engineering, Nanjing Institute of Technology, Nanjing Jiangsu 211167, China
  • Received:2015-05-06 Revised:2015-07-08 Online:2015-10-10 Published:2015-10-14

摘要: 为了改善在某些场景中由于聚焦评价函数非单峰性而造成爬山搜索方法正确率降低、误差增大的问题,设计了一种基于离焦量定性差异度量的自动对焦方法。首先,利用基于空间域的卷积/去卷积变换计算对焦过程中两个不同调焦位置的两幅图像中对应点的离焦量差异值;接着,采用投票策略得出这两幅图像的离焦量差异定性度量;然后,根据离焦量差异定性度量确定对焦搜索方向;最后,按照变步长策略逐渐缩小搜索范围和搜索步长,直至在步长为1时找到合焦位置。在由18倍光学变焦的监控摄像机上采集的3个图像序列上展示了该方法与其他两种典型的基于聚焦评价函数的爬山自动对焦方法的对比,实验结果表明:所提方法在保持爬山搜索法快速、行程比较少等优点的同时,明显提高了在聚焦评价函数单峰性不良的场景中的正确率,降低了误差量,很好地解决了局部极值对于爬山搜索法的影响。

关键词: 自动对焦, 离焦量差异, 聚焦评价函数, 爬山搜索法, S变换

Abstract: In order to solve the problem of low accuracy and big error in hill climb searching method caused by the unimodal focal value function, a new autofocus method based on blur difference qualitative analysis was presented. First, the spatial-domain convolution/deconvolution transform was used to compute the blur difference at every point of two probed images corresponding to two different focus positions. Second, blur difference qualitative measurement of two images was made by voting policy. Then, the searching direction was determined by blur difference qualitative measurement of two probed images. Finally, using the variable step scheme, the searching range was gradually narrowed down and the searching steps was reduced until the best focus position was found. Three image sequences of difference focus positions were collected by an 18X zoom surveillance camera. The experimental result indicates that, compared with two typical methods based on focal value function, the proposed method keeps the advantages of the hill climb searching method with increasing the accuracy and reducing the error, and resolves the influence of local minima.

Key words: autofocus, blur difference, focal value function, hill climb searching method, S transform

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