Detection of surface defects and subsurface defects of polished optics with multisensor image fusion

IF 15.7 Q1 OPTICS
Huanyu Sun, Shiling Wang, Xiao-Xiang Hu, Hongjie Liu, Xiaoyan Zhou, Jin Huang, Xinglei Cheng, Feng Sun, Yubo Liu, Dong Liu
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引用次数: 9

Abstract

Surface defects (SDs) and subsurface defects (SSDs) are the key factors decreasing the laser damage threshold of optics. Due to the spatially stacked structure, accurately detecting and distinguishing them has become a major challenge. Herein a detection method for SDs and SSDs with multisensor image fusion is proposed. The optics is illuminated by a laser under dark field condition, and the defects are excited to generate scattering and fluorescence lights, which are received by two image sensors in a wide-field microscope. With the modified algorithms of image registration and feature-level fusion, different types of defects are identified and extracted from the scattering and fluorescence images. Experiments show that two imaging modes can be realized simultaneously by multisensor image fusion, and HF etching verifies that SDs and SSDs of polished optics can be accurately distinguished. This method provides a more targeted reference for the evaluation and control of the defects of optics, and exhibits potential in the application of material surface research.
基于多传感器图像融合的抛光光学器件表面缺陷和亚表面缺陷检测
表面缺陷和亚表面缺陷是降低光学器件激光损伤阈值的关键因素。由于其空间层叠结构,对其进行准确检测和识别已成为一项重大挑战。提出了一种基于多传感器图像融合的固态硬盘和固态硬盘检测方法。该光学器件在暗场条件下由激光照射,并激发缺陷产生散射光和荧光,由宽视场显微镜中的两个图像传感器接收。利用改进的图像配准和特征级融合算法,从散射和荧光图像中识别和提取不同类型的缺陷。实验表明,通过多传感器图像融合可以同时实现两种成像模式,并且HF刻蚀验证了抛光光学器件的SDs和ssd可以准确区分。该方法为光学缺陷的评价和控制提供了更有针对性的参考,在材料表面研究中具有应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
25.70
自引率
0.00%
发文量
0
审稿时长
13 weeks
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