Abstract

In-situ laser-induced surface damage inspection plays a key role in protecting the large aperture optics in an inertial confinement fusion (ICF) high-power laser facility. In order to improve the initial damage detection capabilities, an in-situ inspection method based on image super-resolution and adaptive segmentation method is presented. Through transfer learning and integration of various attention mechanisms, the super-resolution reconstruction of darkfield images with less texture information is effectively realized, and, on the basis of image super-resolution, an adaptive image segmentation method is designed, which effectively adapts to the damage detection problems under conditions of uneven illumination and weak signal. An online experiment was carried out by using edge illumination and the telescope optical imaging system, and the validity of the method was proved by the experimental results.

© 2022 Chinese Laser Press

Super-resolution and segmentation deep learning for breast cancer histopathology image analysis

Aniwat Juhong, Bo Li, Cheng-You Yao, Chia-Wei Yang, Dalen W. Agnew, Yu Leo Lei, Xuefei Huang, Wibool Piyawattanametha, and Zhen Qiu
Biomed. Opt. Express 14(1) 18-36 (2023)

Automatic optical inspection platform for real-time surface defects detection on plane optical components based on semantic segmentation

Jules Karangwa, Linghua Kong, Dingrong Yi, and Jishi Zheng
Appl. Opt. 60(19) 5496-5506 (2021)

Sub-pixel detection of laser-induced damage and its growth on fused silica optics using registration residuals

Guillaume Hallo, Chloé Lacombe, Romain Parreault, Nadja Roquin, Thierry Donval, Laurent Lamaignère, Jérôme Néauport, and François Hild
Opt. Express 29(22) 35820-35836 (2021)

Deep learning-based optical aberration estimation enables offline digital adaptive optics and super-resolution imaging

Chang Qiao, Haoyu Chen, Run Wang, Tao Jiang, Yuwang Wang, and Dong Li
Photon. Res. 12(3) 474-484 (2024)

Adaptive super-resolution enabled on-chip contact microscopy

Hao Zhang, Xiongchao Chen, Tingting Zhu, Chengqiang Yi, and Peng Fei
Opt. Express 29(20) 31754-31766 (2021)