With the development of optical technology, the application fields of optical elements and optical systems are becoming increasingly extensive; however, localized microscopic defects on the surface of optics affect the corresponding system performance. Therefore, it is necessary to detect defects on the optical surface. With the development of machine vision technology, the microscopic scattering dark-field imaging method of noncontact detection has become an important method for automated surface defect detection. However, in large-aperture fine optics, there are fewer defects on the surface and a large number of sub-aperture images. When using defect images for sub-aperture stitching, the amount of data used for image storage and processing is high and increases with the size of the detection aperture, which requires a significant amount of time for detection. Accordingly, a surface defect stitching method is proposed based on a sub-aperture feature dataset, which uses the constructed sub-aperture feature dataset to realize full-aperture defect stitching, thereby reducing the amount of data stored and processed during the stitching procedure.

To perform full-aperture defect stitching, the defect feature data (defect number, type, shape feature, relative position feature, and sparse matrix data) in the binarized sub-aperture image and its overlapping area image were extracted and the sub-aperture feature dataset could be constructed. Then, based on the constructed feature dataset, for the sub-apertures containing defects in the overlapping areas, the overlapping area matching relationship and the offset parameters between the matched overlapping areas were solved and combined with the initial position calculated from the number of scanning steps of the sub-apertures to obtain an accurate positional relationship between the sub-apertures. For sub-apertures without defects in the overlapping areas, the stitching position was determined based on the theoretical position. Finally, the defect sparse matrix data of each sub-aperture were transformed to the corresponding position using coordinates to realize full-aperture defect stitching. As described herein, the sub-aperture feature dataset was constructed based on the feature data extracted from the images captured using the microscopic scattering dark-field imaging device. Full aperture defect stitching was completed based on the dataset, and then compared with the full-aperture stitching results based on the template matching method to analyze and validate the effectiveness of the proposed research method.

The constructed sub-aperture feature dataset (Table 1) was adopted to calculate the overlapping area matching relationship and the corresponding offset parameter, and compared with the offset calculation results obtained using the template matching method (Table 3). The offset calculation results of this study are basically consistent with those of the template matching method. During the offset calculation in the proposed method, the feature data of all the defective areas extracted from the overlapping areas are used to calculate the offset parameter without the need for comparison between unrelated regions, thereby simplifying the calculation process and improving the corresponding efficiency. Meanwhile, some sub-aperture areas were selected for matching and stitching and compared with the results of the direct stitching method (Fig. 5), showing that the method in this study improves the positional deviation that exists in the defective part of the results of the direct stitching method. Thus, this method can effectively realize the accurate matching of sub-aperture areas. Finally, the full-aperture defect images were obtained using the full-aperture defect stitching method based on the feature dataset and the full-aperture stitching method based on the template matching method (Fig. 6). The number and type of defects and some of the scratch size data in the full-aperture image were detected using the connected component labeling algorithm and the minimum enclosing rectangle algorithm (Fig. 8, Table 4). The defect detection results of the full-aperture images obtained using the two methods are basically consistent. In addition, in the process of full-aperture stitching, when stitching images using the template matching method, the data volume of a single processed sub-aperture image is 1.17 MB, and the corresponding processing and storage data volume increases with each completed image stitching, the data volume of the final full-aperture stitching result image is 9.24 MB. However, the method in this study is based on the feature dataset to complete the full-aperture defect stitching; the data volume of the constructed feature dataset is 3.26 MB, and the final data volume of the full-aperture defect image converted from the full-aperture defect data is 20.9 kB. Thus, the proposed method can effectively obtain full-aperture defects, and the volume of stored and processed data in the stitching process is less than that of the image-based stitching method.

In this study, we extracted the feature data in a defect image to construct a sub-aperture feature dataset and complete full-aperture defect stitching. This was compared with the full-aperture image stitching method based on the template matching method. The results of the defects detected in the full-aperture images corresponding to the two methods are basically consistent. During the full-aperture stitching process, the proposed method uses the feature dataset to determine the relative positional relationship of the sub-aperture and to complete the stitching of full-aperture defects, effectively reducing the volume of processed and stored data in the stitching process compared with the image stitching method based on the template matching method.