中国光学（中英文）2024，Vol.17Issue(6)：1329-1339,11.DOI:10.37188/CO.2024-0079
基于深度降噪卷积神经网络的宽波段共相检测研究
Broad-band co-phase detection based on denoising convolutional neural network
摘要
Abstract
The co-phase error detection of segmented mirrors is currently a critical focus of scientific re-search.Co-phase detection technology based on a broad-band light source solves the problem of long meas-urement times caused by the Shackle-Hartmann method's low target flow rates,thereby improving the accur-acy and range of piston error detection.However,in the application of the current broad-band algorithm,the complex environment and the presence of disturbing factors such as camera perturbations cause the acquired circular aperture diffraction images to contain a certain amount of noise,which leads to a correlation coeffi-cient value below the set threshold,reduces the accuracy of the method,and even makes it ineffective.To solve the problem,we propose a method by integrating an algorithm based on Denoising Convolutional Neural Network(DnCNN)into the broad-band algorithm in order to control the noise interference and retain the phase information of the far-field image.First,the circular hole diffraction image obtained by using MATLAB is used as the training data for DnCNN.After the training,the images with different noise levels are imported into the trained noise reduction model to obtain the denoised image as well as the peak signal-to-noise ratios of the circular hole diffraction images before and after denoising.The structural similarity between the two images and the clear and noiseless image are also obtained.The results indicate that the av-erage structural similarity between the denoised image and the ideal clear image has significantly improved compared to the image before processing,and this achieves an ideal denoising effect,which effectively in-creases the ability of broad-band algorithms to cope with the effects of high noise conditions.This study has strong theoretical significance and application value for exploring the broad-band light source algorithm for applications in practical co-phase detection environments.关键词
拼接镜/piston误差/圆孔衍射/图像降噪/深度降噪卷积神经网络Key words
segmented mirror/piston error/circular diffraction/image denoising/DnCNN分类
数理科学引用本文复制引用
李斌,刘银岭,杨阿坤,陈莫..基于深度降噪卷积神经网络的宽波段共相检测研究[J].中国光学（中英文）,2024,17(6):1329-1339,11.基金项目
国家自然科学基金(No.12103019) （No.12103019）
江西省自然科学青年基金(No.20232BAB211023)Supported by National Natural Science Foundation of China(No.12103019) （No.20232BAB211023）
Natural Science Youth Founda-tion of Jiangxi Province(No.20232BAB211023) （No.20232BAB211023）
