Non-line-of-sight imaging under white-light illumination: a two-step deep learning approach

Non-line-of-sight (NLOS) imaging has received considerable attentions for its ability to recover occluded objects from an indirect view. Various NLOS imaging techniques have been demonstrated recently. Here, we propose a white-light NLOS imaging method that is equipped only with an ordinary camera, and not necessary to operate under active coherent illumination as in other existing NLOS systems. The central idea is to incorporate speckle correlation-based model into a deep neural network (DNN), and form a two-step DNN strategy that endeavors to learn the optimization of the scattered pattern autocorrelation and object image reconstruction, respectively. Optical experiments are carried out to demonstrate the proposed method. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

The white-light NLOS imaging method proposed in this study utilizes a deep neural network to learn the optimization of the scattered pattern autocorrelation and object image reconstruction through a two-step DNN strategy. Optical experiments have demonstrated the effectiveness of this method.