Ghost translation: an end-to-end ghost imaging approach based on the transformer network
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 15, 2022
- Source ID
- 10.1364/oe.478695
Entities
People
- Marlan Scully
- Tao Peng
- Wenhan Ren
- Xiaoyu Nie
Organizations
- Air Force Office of Scientific Research
- Baylor University
- National Science Foundation
- Office of Naval Research
- Princeton University
- Robert A. Welch Foundation
- Texas A&M University
- Xi'an Jiaotong University