Learning-based correction with Gaussian constraints for ghost imaging through dynamic scattering media

Abstract

In this Letter, we propose a learning-based correction method to realize ghost imaging (GI) through dynamic scattering media using deep neural networks with Gaussian constraints. The proposed method learns the wave-scattering mechanism in dynamic scattering environments and rectifies physically existing dynamic scaling factors in the optical channel. The corrected realizations obey a Gaussian distribution and can be used to recover high-quality ghost images. Experimental results demonstrate effectiveness and robustness of the proposed learning-based correction method when imaging through dynamic scattering media is conducted. In addition, only the half number of realizations is needed in dynamic scattering environments, compared with that used in the temporally corrected GI method. The proposed scheme provides a novel, to the best of our knowledge, insight into GI and could be a promising and powerful tool for optical imaging through dynamic scattering media.