Compressive ghost imaging through scattering media with deep learning
Leverage deep learning for computational ghost imaging through scattering media
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. 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.
Publications
Compressive Ghost Imaging through Scattering Media with Deep Learning
F. Li*, M. Zhao*, Z. Tian, F. Willomitzer, O. Cossairt (* joint first authors).
Optics Express 28(12), 17395-17408, 2020
