High-quality ghost imaging through highly complex scattering media with physics-enhanced untrained neural networks

Abstract

Optical imaging through complex media remains a challenge when illumination and detection paths are simultaneously disturbed. In this paper, we report an untrained neural network (UNN) enhanced by a physical model of ghost imaging (GI) to address complex-scattering-induced beam distortions and achieve high-quality object reconstruction. The experimental configuration consists of rotating ground glass (RGG) diffusers placed in front of and behind an object, coupled with a turbidity-varying liquid turbulence chamber in the optical path. Our analysis reveals that a series of dynamic scaling factors critically degrade the performance of GI. To overcome this challenge, speckle patterns induced by complex and dynamic scattering are recorded via the design of a reference beam arm, and a series of single-pixel intensities are collected in the object beam arm. A physics-enhanced UNN is designed and implemented to estimate a series of scaling factors, and a GI formation model is integrated into UNN to ensure the validity of corrected measurements and enable robust reconstruction. Experimental results demonstrate that the proposed method can achieve robust and high-quality object reconstruction through complex scattering media where illumination and detection paths are simultaneously disturbed. The proposed method can open an avenue for overcoming optical scattering challenges in complex scenarios.