Liu Hao, Wang Pengfei, He Xin, Chen Mingyang, Liu Mengge, Xu Ziqin, Jiang Xiaoheng, Peng Xin, Xu Mingliang
Opt Express. 2023 Dec 18;31(26):44113-44126. doi: 10.1364/OE.507875.
Passive non-line-of-sight (NLOS) imaging is a promising technique to enhance visual perception for the occluded object hidden behind the wall. Here we present a data-driven NLOS imaging framework by using polarization cue and long-wavelength infrared (LWIR) images. We design a dual-channel input deep neural network to fuse the intensity features from polarized LWIR images and contour features from polarization degree images for NLOS scene reconstruction. To train the model, we create a polarized LWIR NLOS dataset which contains over ten thousand images. The paper demonstrates the passive NLOS imaging experiment in which the hidden people is approximate 6 meters away from the relay wall. It is an exciting finding that even the range is further than that in the prior works. The quantitative evaluation metric of PSNR and SSIM show that our method as an advance over state-of-the-art in passive NLOS imaging.
被动非视距（NLOS）成像技术有望提升对隐藏在墙后的遮挡物体的视觉感知能力。在此，我们提出一种基于偏振线索和长波红外（LWIR）图像的数据驱动非视距成像框架。我们设计了一个双通道输入深度神经网络，融合偏振LWIR图像的强度特征和偏振度图像的轮廓特征，用于非视距场景重建。为训练该模型，我们创建了一个包含一万余张图像的偏振LWIR非视距数据集。本文展示了被动非视距成像实验，其中隐藏人物距离中继墙约6米。令人兴奋的是，该距离比先前工作中的更远。PSNR和SSIM的定量评估指标表明，我们的方法在被动非视距成像方面优于现有技术。
