Scattering-Assisted Computational Imaging

Abstract

Imaging objects hidden behind an opaque shelter provides a crucial advantage when physically going around the obstacle is impossible or dangerous. Previous methods have demonstrated that is possible to reconstruct the image of a target hidden from view. However, these methods enable the reconstruction by using the reflected light from a wall which may not be feasible in the wild. Compared with the wall, the “plug and play” scattering medium is more naturally and artificially accessible, such as smog and fogs. Here, we introduce a scattering-assisted technique that requires only a remarkably small block of single-shot speckle to perform transmission imaging around in-line-of-sight barriers. With the help of extra inserted scattering layers and a deep learning algorithm, the target hidden from view can be stably recovered while the directly uncovered view is reduced to 0.097% of the whole field of view, successfully removing the influence of large foreground occlusions. This scattering-assisted computational imaging has wide potential applications in real-life scenarios, such as covert imaging, resuming missions, and detecting hidden adversaries in real-time.

Keywords

• non-line-of-sight imaging
• single-shot speckle
• scattering-assisted
• field-of-view
• remarkably small speckle block
• deep learning