Fused silica optical elements are widely used in large high-power laser devices, and the subsurface defects of optical elements directly affect the laser damage threshold and imaging quality. These defects are distributed within a few to tens of microns from the surface and cannot be detected by conventional imaging methods. The characteristics of small size, low density and wide distribution range make it difficult to detect nondestructively, effectively and quickly with conventional methods. In order to solve the above problems, an experimental system was built based on the principles of photoluminescence and dark field scattering. Use the different characteristics of fluorescence image and scattering image to carry out the research of subsurface defect detection. First, preprocess the original image. Second, calculate the offset of adjacent images, perform background homogenization processing on the image, and stitch the sub-aperture images, and then segment the full-aperture image. Then, a more effective subsurface defect extraction algorithm is proposed. Finally, etch the sample with HF and observe it under a microscope. Experimental results show that this method can detect weak defects on the surface and sub-surface at the same time, and can effectively separate individual sub-surface defects. This method has the advantages of lossless, fast and high precision. Moreover, based on a large amount of data, analysis and summary of the causes of defects, distribution characteristics, etc.. This method can provide certain guidance for the evaluation of laser damage threshold and processing technology of optical components in high-power laser devices.
In large high-power laser devices,the surface and subsurface defects of fused silica optical components directly affect the laser damage threshold and imaging quality. In this paper, fluorescence imaging technology is used to obtain images of defects in the subsurface layer of optical components that will absorb laser. Because the original image has the characteristics of sparse signal, weak intensity, low contrast, etc. In order to efficiently and reliably evaluate the surface and subsurface defects, this paper proposes a weak and small defect detection method based on local adaptive contrast enhancement and seed region growth. Firstly, the local adaptive contrast enhancement method is used to enhance the contrast of the original image. Secondly, the method of bilateral filtering is used to denoise. Thirdly, seed region growth method is used to segment the defective regions and perform morphological processing. Finally, defect detection is performed. The experiment uses different segmentation methods to detect images in different regions. The results show that this method can significantly enhance the contrast of the original fluorescence image, and detect pixel-level defects, and the detection rate is stable at about 95%. Meanwhile, the reasons, size distribution and other characteristics of the sub-surface defects of fused silica optical components are analyzed. This paper provides a nondestructive method of detecting weak and small defects in the subsurface layer of the optical element faster and higher accuracy.
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