High speed ghost imaging based on a heuristic algorithm and deep learning*

We report an overlapping sampling scheme to accelerate computational ghost imaging for imaging moving targets, based on reordering a set of Hadamard modulation matrices by means of a heuristic algorithm. The new condensed overlapped matrices are then designed to shorten and optimize encoding of the overlapped patterns, which are shown to be much superior to the random matrices. In addition, we apply deep learning to image the target, and use the signal acquired by the bucket detector and corresponding real image to train the neural network. Detailed comparisons show that our new method can improve the imaging speed by as much as an order of magnitude, and improve the image quality as well.

Automated summary

An overlapping sampling scheme is proposed to accelerate computational ghost imaging for moving targets. The method reorders Hadamard modulation matrices and uses deep learning to improve imaging speed and quality by training a neural network with acquired signals and real images from bucket detectors. Detailed comparisons demonstrate a significant improvement in imaging speed and quality using this new approach.