Ghost translation: an end-to-end ghost imaging approach based on the transformer network. Academic Article uri icon

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.

published proceedings

  • Opt Express

author list (cited authors)

  • Ren, W., Nie, X., Peng, T., & Scully, M. O.

citation count

  • 0

complete list of authors

  • Ren, Wenhan||Nie, Xiaoyu||Peng, Tao||Scully, Marlan O

publication date

  • December 2022