Author Affiliations
Abstract
1 Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
2 Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Non-line-of-sight (NLOS) imaging is an emerging technique for detecting objects behind obstacles or around corners. Recent studies on passive NLOS mainly focus on steady-state measurement and reconstruction methods, which show limitations in recognition of moving targets. To the best of our knowledge, we propose a novel event-based passive NLOS imaging method. We acquire asynchronous event-based data of the diffusion spot on the relay surface, which contains detailed dynamic information of the NLOS target, and efficiently ease the degradation caused by target movement. In addition, we demonstrate the event-based cues based on the derivation of an event-NLOS forward model. Furthermore, we propose the first event-based NLOS imaging data set, EM-NLOS, and the movement feature is extracted by time-surface representation. We compare the reconstructions through event-based data with frame-based data. The event-based method performs well on peak signal-to-noise ratio and learned perceptual image patch similarity, which is 20% and 10% better than the frame-based method.
non-line-of-sight imaging event camera event-based representation Chinese Optics Letters
2023, 21(6): 061103
北京理工大学光电学院光电成像技术与系统教育部重点实验室，北京 100081
针对高浑浊水体环境散射严重、目标成像不清晰和对比度低的问题，在传统UNet结构的基础上，结合偏振成像理论，提出了基于残差UNet（Mu-UNet）网络的水下Mueller矩阵图像去散射算法。该算法依据Mueller矩阵图像提供的目标强度信息和偏振信息，建立不同浑浊度水下多个目标物的图像数据集。在UNet基础上引入残差模块，利用构建的Mu-UNet网络提取目标底层信息，学习标签图像特征，重建出对比度高、细节信息更丰富的水下目标复原图像。对比实验结果表明，所提算法在峰值信噪比方面较原图提升了89.40%，结构相似度提升了82.37%。相比传统算法和UNet网络，所提算法得到的复原图像具有更明显的去散射效果，细节更精细，为水下偏振清晰成像探测提供了一种新思路。
图像处理 水下散射 偏振成像 Mueller矩阵 残差UNet 目标探测 光学学报
2022, 42(24): 2410001
