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Title: 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.

 
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Award ID(s):
2013771
NSF-PAR ID:
10385762
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Optical Society of America
Date Published:
Journal Name:
Optics Express
Volume:
30
Issue:
26
ISSN:
1094-4087; OPEXFF
Page Range / eLocation ID:
Article No. 47921
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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