Passive Non-Line-of-Sight Imaging with Parallel Encoder

Passive non-line-of-sight (NLOS) imaging has developed rapidly in recent years. However, existing models generally suffer from low-quality reconstruction due to the severe loss of information during the projection process. In this paper, we introduce ParaEncodeNet, an NLOS imaging method for reconstructing high-quality, complex hidden scenes. Our approach utilizes a reconstruction network with parallel encoder to bridge the distribution gap between projection images and hidden images. The parallel encoder employs a codebook pretrained on a natural image dataset to construct a discrete prior, enabling the efficient encoding of projection images into hidden images. Moreover, we apply pixel-level constraints to the projection images to further reduce noise and distortion during reconstruction. Extensive experiments on a large-scale passive NLOS dataset have effectively demonstrated the superiority of our method over existing approaches, achieving a 1.2 dB increase in the Peak Signal-to-Noise Ratio (PSNR) metric. This validates the effectiveness and robustness of our proposed model in improving reconstruction quality and handling complex scenes.