Far-field super-resolution ghost imaging with a deep neural network constraint
Chinese Academy of Sciences · Shanghai Institute of Optics and Fine Mechanics · +3 more institutions
Abstract
Ghost imaging (GI) facilitates image acquisition under low-light conditions by single-pixel measurements and thus has great potential in applications in various fields ranging from biomedical imaging to remote sensing. However, GI usually requires a large amount of single-pixel samplings in order to reconstruct a high-resolution image, imposing a practical limit for its applications. Here we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural network. The resulting hybrid neural network does not need to pre-train on any dataset, and allows the reconstruction of a far-field image with the resolution beyond the diffraction limit.…
Citation impact
- FWCI
- 33.20
- Percentile
- 100%
- References
- 48
Authors
7- FWFei WangCorresponding
Chinese Academy of Sciences, Shanghai Institute of Optics and Fine Mechanics, University of Chinese Academy of Sciences
- CWChenglong Wang
Chinese Academy of Sciences, Shanghai Institute of Optics and Fine Mechanics, University of Chinese Academy of Sciences
- MCMingliang Chen
Chinese Academy of Sciences, Shanghai Institute of Optics and Fine Mechanics, University of Chinese Academy of Sciences
- WGWenlin Gong
Chinese Academy of Sciences, Shanghai Institute of Optics and Fine Mechanics, University of Chinese Academy of Sciences
- YZYu Zhang
Chinese Academy of Sciences, Shanghai Institute of Optics and Fine Mechanics
Topics & keywords
- Ghost imaging
- Constraint (computer-aided design)
- Ranging
- Image (mathematics)
- Artificial neural network
- Limit (mathematics)
- Image resolution
- Iterative reconstruction