光栅型波前曲率传感器光斑自动定位提取算法

Automatic Spot Location and Extraction Algorithm for Grating Wavefront Curvature Sensor

针对光栅型波前曲率传感器,提出一种基于最大类间方差法和质心法相结合的光斑自动定位提取算法,并进行了实验验证。使用最大类间方差法对原始光强分布图像进行二值化;根据质心坐标对二值化图像进行分割,得到两幅含有单个光斑的二值化图像;分别计算两幅图像的质心坐标,作为原始光强图像中的两个光斑中心,并在原始图像中进行光斑提取。搭建了基于离轴菲涅耳波带片的光栅型波前曲率传感器实验装置,采用上述方法自动提取光斑,采用拉普拉斯算子本征模式法进行波前复原,并与哈特曼波前传感器的测量结果进行了比较。研究结果表明该算法能够自动提取光栅型波前曲率传感器的光斑,定位误差小于4pixel。

An automatic spot location and extraction algorithm based on the combination of the Otsu method and the centroid method is proposed herein for the grating wavefront curvature sensor and verified through experiments.The original intensity distribution image is first binarized by using Otsu method.Then,the binary image is then segmented into two binary images according to the centroid coordinates such that each binary image contains one spot.Finally,the centroid coordinates of the two images are calculated as two spot centers in the original intensity image,while the two spots in the original image are extracted from the original intensity image.An experimental setup of the grating wavefront curvature sensor is built based on an off-axis Fresnel zone plate to verify the effectiveness of the proposed algorithm.In the experiment,the spots are extracted automatically by using the above algorithm;then,the wavefront is restored by using Laplacian eigenfunctions.The results are compared with those of the Hartmann wavefront sensor.The experimental results demonstrate that the proposed algorithm can extract spots automatically with an error less than 4 pixel.