Denoising ghost imaging under a small sampling rate via deep learning for tracking and imaging moving objects

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.