Learning to image and track moving objects through scattering media via speckle difference
Kai Ma, Xia Wang, Si He, Xin Zhang, Yixin Zhang
Abstract
The imaging and tracking of moving objects through scattering media is a challenge due to the serious degradation of optical information. However, the reconstruction fidelity for non-sparse objects is inadequate and the displacement information lack the quantitative description of moving objects. In this study, we propose a deep learning method to decode the shape and displacement information of moving objects on the plane perpendicular to the system’s optical axis from one-frame speckle difference pattern. The proposed method was verified via experiments and it was found to be viable for imaging moving objects with different complexity and sparsity. The superiority of the method was demonstrated via comparison experiments. Moreover, it accurately tracked moving objects at a pixel level.