一体化摄像机的自动聚焦过程就是镜头按照自动聚焦搜索算法寻找图像清晰度评价函数最大值的过程，自动聚焦搜索算法对自动聚焦过程的实时性和准确率影响很大。为了解决现有算法在光源场景和无细节场景下聚焦失败的问题，提出了一种基于场景预测的聚焦搜索算法，该算法对成像场景进行智能分析、判断并分类，在不同的场景下采用不同的搜索策略，提高了自动聚焦搜索的稳定性和准确率。同时，针对现有算法在散焦区判断聚焦曲线峰值方向困难的问题，引入模糊度评价模型，该模型能够准确判断聚焦曲线峰值方向，分析实验数据发现，新的算法在散焦区的聚焦速度提升了24.3%。实验证明，新的算法在基于海思Hi3518A处理器搭建的一体化摄像机成像系统中具有很高的应用价值。
Abstract
The auto-focusing process applied to integrated camera is described as the lens looking for the maximum value of image definition evaluation function according to auto-focusing search algorithm. The auto-focusing search algorithm has a great influence on the focusing time and focusing accuracy of the whole auto-focusing process. In order to solve the problem that the existing algorithms fail to focus under light source scenes and no-detail scenes, a focus search algorithm based on scene prediction is proposed.The algorithm can intelligently analyze, judge and classify imaging scenes, and uses different search strategies in different scenarios to improve the stability and accuracy of auto-focus searching.At the same time, aiming at the problem that the existing algorithm is difficult to determine the direction of the peak of focus curve in defocus area, an fuzzy degree evaluation model is introduced, which can accurately determine the peak direction of the focus curve. Analysis of experimental data shows that the new algorithm has increased the focusing speed in the defocus area by 24.3%. Experiments verify that the new algorithm has high application value in the integrated camera imaging system based on Hass Hi3518A processor.
关键词
聚焦搜索算法 /
一体化摄像机 /
自动聚焦 /
聚焦曲线分析 /
模糊度评价 /
图像信号处理
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Key words
image signal processing /
focusing searching algorithm /
integrated camera /
auto-focusing /
focusing curve analysis /
fuzzy degree evaluation
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