强散粒噪声下聚焦评价函数的研究  

Research of Focusing Evaluation Function Under a Strong Shot-Noise Background

作　　者：李丽宏[1,2] 张明路[1] 

机构地区：[1]河北工业大学机械学院,天津300130 [2]河北工程大学信电学院,河北邯郸056038

出　　处：《激光与光电子学进展》2012年第11期116-121,共6页Laser & Optoelectronics Progress

基　　金：国家863计划(2007AA04Z229);河北省自然科学青年基金(F2012202074)资助课题

摘　　要：针对强散粒噪声背景下图像聚焦精度差的问题,对聚焦评价函数处理过程进行了深入的研究。在对常规图像系列进行实验的基础上,得出改进的拉普拉斯算子(SML)与拉普拉斯算子聚焦评价函数各方面性能较优。针对强散粒噪声图像,提出在中值滤波后,采用分水岭算法对滤波图像进行过分割处理,对分割的各小区域块,求取其均值,用均值代替各区域内像素灰度值,能较好地抑制噪声,再利用SML或拉普拉斯算子聚焦评价函数进行自动聚焦。实验表明,针对强散粒噪声图像提出的聚焦评价处理方法,其精度接近于非强散粒噪声图像系列,同时此算法具有较好的稳健性。Aimed at focusing precision problems under a strong shot noisy background image, the processing of focusing evaluation function is deeply studied. Based on the experiment of conventional image sequences, sum of modified ]aplacian （SML） and laplacian focusing evaluation functions have better performance. Aimed at strong shot noisy image sequence, an algorithm is provided that this image sequence is implemented with median filtering. Then this sequence is over-segmented using the watershed algorithm in order to obtain area blocks. The pixel value is substituted by the mean value of this area block, which can reduce the shot noise＇s influence. Finally, this image sequence is processed by SML or laplacian focusing evaluation function. Experimental results show that this processing＇s accuracy is close to the image sequence without strong shot noise. This algorithm has good robustness.

关 键 词：图像处理 聚焦评价函数 分水岭算法 强散粒噪声 中值滤波 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]