Compressive ghost imaging through scattering media with deep learning
Abstract
Imaging through scattering media is challenging since the signal to noise ratio (SNR) of the reflection can be heavily reduced by scatterers. Single-pixel detectors (SPD) with high sensitivities offer compelling advantages for sensing such weak signals. In this paper, we focus on the use of ghost imaging to resolve 2D spatial information using just an SPD. We prototype a polarimetric ghost imaging system that suppresses backscattering from volumetric media and leverages deep learning for fast reconstructions. In this work, we implement ghost imaging by projecting Hadamard patterns that are optimized for imaging through scattering media. We demonstrate good quality reconstructions in highly scattering conditions using a 1.6% sampling rate.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 26, 2020
- Source ID
- 10.1364/oe.394639
Entities
People
- Fengqiang Li
- Florian Willomitzer
- Ming Zhao
- Oliver Cossairt
- Zhiming Tian
Organizations
- Defense Advanced Research Projects Agency
- National Natural Science Foundation of China
- National Science Foundation