Ghost translation

TL;DR

This paper introduces a transformer-based deep neural network for ghost imaging that translates single-pixel detector signals into high-quality 2D images at very low sampling ratios, demonstrating robustness to noise.

Contribution

It presents a novel sequence transduction approach using transformers for ghost imaging, enabling end-to-end translation of signals into images with minimal sampling.

Findings

High-quality images at 2% sampling ratio

Robustness to noise interference

Effective with both designed and random speckle patterns

Abstract

Artificial intelligence has recently been widely used in computational imaging. The deep neural network (DNN) improves the signal-to-noise ratio of the retrieved images, whose quality is otherwise corrupted due to the low sampling ratio or noisy environments. This work proposes a new computational imaging scheme based on the sequence transduction mechanism with the transformer network. The simulation database assists the network in achieving signal translation ability. The experimental single-pixel detector's signal will be `translated' into a 2D image in an end-to-end manner. High-quality images with no background noise can be retrieved at a sampling ratio as low as 2%. The illumination patterns can be either well-designed speckle patterns for sub-Nyquist imaging or random speckle patterns. Moreover, our method is robust to noise interference. This translation mechanism opens a new direction for DNN-assisted ghost imaging and can be used in various computational imaging scenarios.