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arXiv · 2209.15012

Ghost translation

Wenhan Ren·Xiaoyu Nie·Tao Peng·Marlan O. Scully
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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.

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Wenhan Ren, Xiaoyu Nie, Tao Peng, Marlan O. Scully. 2022-09-29. Ghost translation. https://doi.org/10.1364/oe.478695

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