Non-blind super-resolution reconstruction for laser-induced damage dark-field imaging of optical elements  

作　　者：王倩 陈凤东 韩越越 曾发 路程 刘国栋 Qian Wang;Fengdong Chen;Yueyue Han;Fa Zeng;Cheng Lu;Guodong Liu(Instrument Science and Technology,Harbin Institute of Technology,Harbin 150001,China;Research Center of Laser Fusion,China Academy of Engineering Physics,Mianyang 621900,China)

机构地区：[1]Instrument Science and Technology,Harbin Institute of Technology,Harbin 150001,China [2]Research Center of Laser Fusion,China Academy of Engineering Physics,Mianyang 621900,China

出　　处：《Chinese Optics Letters》2024年第4期36-41,共6页中国光学快报（英文版）

摘　　要：The laser-induced damage detection images used in high-power laser facilities have a dark background,few textures with sparse and small-sized damage sites,and slight degradation caused by slight defocus and optical diffraction,which make the image superresolution(SR)reconstruction challenging.We propose a non-blind SR reconstruction method by using an exquisite mixing of high-,intermediate-,and low-frequency information at each stage of pixel reconstruction based on UNet.We simplify the channel attention mechanism and activation function to focus on the useful channels and keep the global information in the features.We pay more attention on the damage area in the loss function of our end-toend deep neural network.For constructing a high-low resolution image pairs data set,we precisely measure the point spread function(PSF)of a low-resolution imaging system by using a Bernoulli calibration pattern;the influence of different distance and lateral position on PSFs is also considered.A high-resolution camera is used to acquire the ground-truth images,which is used to create a low-resolution image pairs data set by convolving with the measured PSFs.Trained on the data set,our network has achieved better results,which proves the effectiveness of our method.

关 键 词：laser-induced damage image superresolution image segmentation 

分 类 号：TN249[电子电信—物理电子学] TP391.41[自动化与计算机技术—计算机应用技术]