叠焦大景深成像中的聚焦评价算子性能评估方法  

Performance Evaluation Method for Focusing Evaluation Operator in Superposed Large Depth Imaging

作　　者：于春水 卢荣胜[1] Yu Chunshui;Lu Rongsheng(College of Instrument Science and OptoElectronic Engineering,Hefei University of Technology,Hefei 230009,Anhui,China)

机构地区：[1]合肥工业大学仪器科学与光电工程学院,安徽合肥230009

出　　处：《激光与光电子学进展》2022年第14期283-289,共7页Laser & Optoelectronics Progress

基　　金：国家重点研发计划(2018YFB2003801);国家自然科学基金(51875164)。

摘　　要：聚焦评价算法是叠焦大景深成像的核心,针对聚焦评价算子性能评估方面的实验需求,提出了一种基于图像序列采样点聚焦评价散点图高斯拟合的聚焦评价算子性能评估方法,对已有的聚焦评价算子进行了性能评估实验。将传统的图像清晰程度指标加以改造,提出了一种梯度加权图像锐度算子,并采用实采图像和模拟图像分别对比了所提算子与现有算子的性能差异。研究结果对于实施叠焦测量有一定参考意义。Focusing evaluation algorithms are the core of the superposed large depth of field imaging.Aiming at the experimental requirements of evaluating the performance of the focusing evaluation operator,we proposed a focusing evaluation operator performance evaluation method using image sequence sampling-point focusing evaluation and scatter plot Gaussian fitting.The performance evaluation experiments are performed on the existing focusing evaluation operators.Furthermore,we proposed a gradient-weighted image sharpness operator by modifying the traditional image sharpness index.The performance difference between the new and existing operators is compared using real and simulated images.The research results have certain reference significance for the implementation of stacking measurement.

关 键 词：机器视觉 叠焦测量 大景深 聚焦评价 高斯拟合 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]