Open AccessDenoising ghost imaging under a small sampling rate via deep learning for tracking and imaging moving objects
Author(s) -
Hong-Kang Hu,
Shuai Sun,
Huizu Lin,
Liang Jiang,
Wei-Tao Liu
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.412597
Subject(s) - artificial intelligence , ghost imaging , computer science , computer vision , deep learning , noise reduction , sampling (signal processing) , image quality , tracking (education) , object detection , pattern recognition (psychology) , image (mathematics) , filter (signal processing) , psychology , pedagogy
Ghost imaging (GI) usually requires a large number of samplings, which limit the performance especially when dealing with moving objects. We investigated a deep learning method for GI, and the results show that it can enhance the quality of images with the sampling rate even down to 3.7%. With a convolutional denoising auto-encoder network trained with numerical data, blurry images from few samplings can be denoised. Then those outputs are used to reconstruct both the trajectory and clear image of the moving object via cross-correlation based GI, with the number of required samplings reduced by two-thirds.