Ghost translation: an end-to-end ghost imaging approach based on the transformer network

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.(c) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

Artificial intelligence is widely used in computational imaging to improve the quality and signal-to-noise ratio of images affected by low sampling ratio or noisy environments. This study proposes a new computational imaging scheme based on the sequence transduction mechanism with the transformer network, which can retrieve high-quality images and is robust to noise interference.