为了提高自动调焦算法在动态环境下的性能,对调焦评价函数和调焦搜索策略进行了研究。在分析人类视觉系统特性研究成果的基础上, 提出一种基于8方向Sobel算子边缘加权的调焦评价函数。同时, 为了克服传统爬山法搜索速度慢的缺点, 采用自适应变步长极值搜索策略, 通过仿真实验可知, 提出的8方向Sobel边缘检测算子具备良好的边缘提取效果, 同时基于此算子结合人类视觉机制特性给予各边缘不同权重系数计算的调焦评价函数,比经典两方向Sobel算子调焦评价函数具备更好的抗干扰能力。最后搭建基于液体透镜的实验平台, 在动态环境下验证改进算法的性能。实验结果表明, 提出的自动调焦算法在动态环境下调焦准确率达到97.5%。

Aimed at improving performance of automatic focusing algorithm in dynamic environment, this paper describes recent studies of focusing evaluation function and search strategy. On the basis of analysis of characteristics of human visual system, new focusing evaluation function based on eight direction Sobel operator edge weighting is proposed. At the same time, adaptive variable step search strategy is used in order to overcome the disadvantage of slow speed of traditional climbing method. Simulation experiment results show eight direction Sobel edge detection operator has good edge detection effect, and focusing evaluation function has better anti-interference ability than traditional two direction Sobel operator focusing evaluation function, which is calculated by this operator combined with characteristics of human visual system giving the edges with different weight coefficients. Finally, an experimental platform based on liquid lens is set up, which verifies the performance of improved auto focus algorithm in dynamic environment. Experimental results show focusing accuracy with proposed algorithm can achieve 97.5% in dynamic environment.