Saylor J R, Sivasubramanian N A
Department of Mechanical Engineering, Clemson University, Clemson, South Carolina 29634, USA.
Appl Opt. 2007 Aug 1;46(22):5352-67. doi: 10.1364/ao.46.005352.
Optical imaging of raindrops provides important information on the statistical distribution of raindrop size and raindrop shape. These distributions are critical for extracting rainfall rates from both dual- and single-polarization radar signals. A large number of raindrop images are required to obtain these statistics, necessitating automatic processing of the imagery. The accuracy of the measured drop size depends critically on the characteristics of the digital image processing algorithm used to identify and size the drop. Additionally, the algorithm partially determines the effective depth of field of the camera/image processing system. Because a large number of drop images are required to obtain accurate statistics, a large depth of field is needed, which tends to increase errors in drop size measurement. This trade-off between accuracy and depth of field (dof) is also affected by the algorithm used to identify the drop outline. In this paper, eight edge detection algorithms are investigated and compared to determine which is best suited for accurately extracting the drop outline and measuring the diameter of an imaged raindrop while maintaining a relatively large depth of field. The algorithm which overall gave the largest dof along with the most accurate estimate of the size of the drop was the Hueckel algorithm [J. Assoc. Comput. Mach. 20, 634 (1973)].
雨滴的光学成像提供了有关雨滴大小和雨滴形状统计分布的重要信息。这些分布对于从双极化和单极化雷达信号中提取降雨率至关重要。需要大量的雨滴图像来获取这些统计数据，因此有必要对图像进行自动处理。测量的雨滴大小的准确性关键取决于用于识别雨滴并确定其大小的数字图像处理算法的特性。此外，该算法部分决定了相机/图像处理系统的有效景深。由于需要大量的雨滴图像来获得准确的统计数据，所以需要较大的景深，而这往往会增加雨滴大小测量的误差。这种在准确性和景深之间的权衡也受到用于识别雨滴轮廓的算法的影响。在本文中，研究并比较了八种边缘检测算法，以确定哪种算法最适合在保持相对较大景深的同时准确提取雨滴轮廓并测量成像雨滴的直径。总体而言，给出最大景深以及对雨滴大小最准确估计的算法是休克尔算法[《美国计算机协会杂志》20, 634 (1973)]。
