Computational ghost imaging with compressed sensing based on a convolutional neural network

• 作 者: Hao Zhang Deyang Duan
• 机 构: School of Physics and Physical Engineering,Qufu Normal University,Qufu 273165,China;School of Physic
• 出 处: 中国光学快报（英文版）
• 发表日期: 2021年10期
• 关键词: computational ghost imaging compressed sensing convolutional neural network

Computational ghost imaging[CGI]has recently been intensively studied as an indirect imaging technique.However,the image quality of CGI cannot meet the requirements of practical applications.Here,we propose a novel CGI scheme to significantly improve the imaging quality.In our scenario,the conventional CGI data processing algorithm is optimized to a new compressed sensing[CS]algorithm based on a convolutional neural network[CNN].CS is used to process the data collected by a conventional CGI device.Then,the processed data are trained by a CNN to reconstruct the image.The experimental results show that our scheme can produce higher quality images with the same sampling than conven-tional CGI.Moreover,detailed comparisons between the images reconstructed using the deep learning approach and with conventional CS show that our method outperforms the conventional approach and achieves a ghost image with higher image quality.