大型高功率激光驱动装置中,激光能量密度及系统运行速度主要受终端光学元件损伤增长的限制。为高效、精确地检测元件的损伤状态,提出了一种基于局部信噪比的自适应差异窗过滤算法。该算法通过设计一种作用在像素点上的窗函数,以关联邻域点的像素值强弱完成目标点或背景点的判断,从而完成种子图像的阈值化,最后通过对种子图像区域生长完成损伤分割。为验证算法的有效性,搭建了在线检测模拟平台以获取损伤样品图像,并使用该算法对图像进行处理。结果表明:对直径50 μm以上的损伤点,算法的平均识别率在99%以上,达到了高功率激光驱动系统对微小损伤检测的精度要求。因其不需要依据经验设定种子图像的阈值,与现有局部信噪比算法相比具有更高的自动化程度。

In large-scale high-power laser devices, the laser energy density and the system operating speed are mainly limited by the damage growth of the terminal optical element. An adaptive differential window filtering method based on local signal-to-noise ratio is proposed in order to detect the damage state of the element efficiently and accurately. By designing a window function which acts on pixels, the algorithm can judge the target point or background point according to the pixel value and the values of its neighborhood point. And then the segmentation of the seed image is completed. Finally, the damage segmentation is completed by the growth of the seed region. In order to verify the effectiveness of the algorithm, we built an on-line detection simulation platform to obtain the damaged sample image, and the new method mentioned above is used to process the image. The results show that the average recognition rate of the method is above 99% for the damage points with the diameter above 50 μm, which meets the requirement of high-power laser drive system for the detection of small damage. Because the algorithm does not require setting the threshold of the seed image based on experience, it is more automated than the existing local signal-to-noise ratio algorithm.