强激光与粒子束2024，Vol.36Issue(7)：146-154,9.DOI:10.11884/HPLPB202436.240030
采用卷积神经网络提高鬼成像的边缘质量
Edge quality improvement of ghost imaging based on convolutional neural network
摘要
Abstract
Scatter-shift ghost imaging edge extraction methods require multiple sampling of the object to obtain a high quality edge map.To solve the problem of many samples and long time when extracting the edge of the object by scatter-shift ghost imaging,convolutional neural network is adopted to the edge extraction experiment of ghost imaging.Firstly,the unknown image is irradiated by Walsh scattering,the sampled signal collected by the barrel detector is input to the ghost imaging edge extraction network as the image feature information,finally the edge information map of the detected object is directly outputted by the trained network,and the output of the convolutional neural network is optimized by using the non-maximum value suppression algorithm.The experimental results show that for the reconstructed object of 128×128 pixels,the signal-to-noise ratio and structural similarity index of the ghost imaging edge extraction network output edge pattern are 5 times and 2 times higher than that of the scatter-shift ghost imaging respectively when the sampling number is 1600,which successfully improves the quality of the ghost imaging edge extraction under the low sampling rate and reduces the sampling time.The ghost imaging edge extraction scheme using convolutional neural network is conducive to fast and high-quality edge detection of ghost imaging in practical applications of object recognition and security inspection.关键词
边缘提取/鬼成像/卷积神经网络/深度学习/非极大值抑制Key words
edge detection/ghost imaging/convolutional neural network/deep learning/non-maximum suppression分类
数理科学引用本文复制引用
张航宇,吴仪,赵帅,冯国英..采用卷积神经网络提高鬼成像的边缘质量[J].强激光与粒子束,2024,36(7):146-154,9.基金项目
国家重点研发计划项目(2022YFB3606304) （2022YFB3606304）
国家自然科学基金项目(U2230129) （U2230129）
