摘要
聚焦扫描成像模型作为一种有效的计算成像手段,可以实现大景深拓展。从模糊函数理论出发,提出聚焦扫描的逆滤波计算成像模型,并对景深拓展性能进行分析。根据光学系统模糊函数与光学传递函数的关系,利用聚焦误差推演出聚焦扫描成像的光学传递函数。对光学传递函数的近似三维空间不变性给出理论分析,并基于此光学传递函数建立聚焦扫描的逆滤波计算成像模型。以具体成像模型为例,利用HOPKINS判据,分析了不同扫描范围对该成像模型景深拓展性能的影响。通过数值模拟验证了聚焦扫描成像光学模型传递函数的正确性;利用逆滤波方法对扫描范围分别为0.09,0.18,0.36mm的聚焦扫描成像模型进行计算成像,并对成像效果进行了分析比较。结果表明:聚焦扫描成像模型可以实现景深拓展;随着扫描范围增加,景深拓展性能提高。
As an effective means of computational imaging,focal sweep imaging model can extend the depth of field.Based on the ambiguity function theory,we propose an inverse filtering computational imaging model based on focal sweep mode and analyze the expand performance of the depth of field.We obtain the optical transfer function of focal sweep imaging using focus error based on the relationship between the ambiguity function and the optical transfer function.A theoretical analysis of the approximate three-dimensional space invariance of the optical transfer function is given.Based on the optical transfer function,we establish an inverse filtering computational imaging model of focal sweep.Taking a concrete imaging model as an example,we analyze the influence of different scanning ranges on the expand performance of depth of field of focal sweep imaging model based on the HOPKINS criterion.Through numerical simulation,we verify the correctness of the optical transfer function of focal sweep imaging model.We analyze and compare the imaging results of focal sweep imaging model of different scanning ranges(0.09,0.18,0.36 mm)based on inverse filtering model.The analysis shows that the depth of field can be extended by focal sweep imaging model;the larger the sweep distance,the better the performance of the depth of field of focal sweep imaging model.
作者
高姗
邱钧
刘畅
Gao Shan;Qiu Jun;Liu Chang(Institute of Applied Mathematics,Beijing Information Science and Technology University,Beijing 100101,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2018年第7期264-272,共9页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61372150)
高动态导航技术北京市重点实验室开放课题(HDN2017004)
关键词
成像系统
聚焦扫描
计算成像
模糊函数
逆滤波
imaging system
focal sweep
computational imaging
ambiguity function
inverse filtering