现有的通过散射介质的目标分析主要针对静止目标，同时，环境或外部亮度条件对目标分析结果有着至关重要的影响，这就对图像采集设备的动态范围、时间分辨率等性能提出了极高要求。事件相机由于其高动态范围、高时间分辨率与低延迟等特点为应对上述问题提供了新的解决思路。本文针对低照度情况下运动目标透过薄散射介质探测效果差的问题，利用事件相机开展了透过薄散射介质的目标探测的研究。采用V2E算法利用灰度散斑制备“事件散斑”数据集，并采用ResNet分类网络进行目标分类，获得了94.27%的十分类精确率。实验结果表明，使用事件流在分析通过散射介质的目标信息方面有着巨大的发展潜力。

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.

建立了事件相机空间目标探测灵敏度模型，分析了影响事件相机观测灵敏度的影响因素，得到了可观测极限星等与阈值的线性关系，并通过望远镜进行了外场观星实验。结果表明事件相机可以进行空间目标探测，同时验证了空间目标探测灵敏度模型的准确性。进一步明确了阈值对事件相机空间目标探测的影响，为基于事件相机的空间目标观测及其他相关研究提供了理论基础。

针对传统帧模式星敏感器在高动态（≥3°/s）下星点质心提取精度低或提取失败的问题，提出一种基于事件的高动态星敏感器星点提取方法。该方法基于事件相机低延迟、高时间分辨率的特性，解决了高动态条件下的运动模糊问题。首先，提出基于时空密度的噪声去除方法，针对事件流进行降噪处理，平均去噪精度在0.85以上，提高了事件信噪比。其次，提出基于均值漂移的星点定位方法，计算星点事件簇的中心作为星点质心。最后，通过不同条件下的仿真实验，验证了所提去噪方法的有效性和鲁棒性，且角速度达到20°/s时，仍可完成星点提取，角速度在3°/s~10°/s时，平均质心误差小于0.04 pixel，角速度达到15°/s时，平均质心误差小于0.1 pixel。

High-speed target three-dimensional (3D) trajectory and velocity measurement methods have important uses in many fields, including explosive debris and rotating specimen trajectory tracking. The conventional approach uses a binocular system with two high-speed cameras to capture the target’s 3D motion information. Hardware cost for the conventional approach is high, and accurately triggering several high-speed cameras is difficult. Event-based cameras have recently received considerable attention due to advantages in dynamic range, temporal resolution, and power consumption. To address problems of camera synchronization difficulties, data redundancy, and motion blur in high-speed target 3D trajectory measurement, this Letter proposes a 3D trajectory measurement method based on a single-event camera and a four-mirror adaptor. The 3D trajectory and velocity of a particle flight process and a marker on a rotating disc were measured with the proposed method, and the results show that the proposed method can monitor the operational state of high-speed flying and rotating objects at a very low hardware cost.

针对事件相机输出的事件流中存在大量噪声的问题，介绍了一种基于概率无向图模型的事件流去噪算法。算法基于目标变化在时间和空间上具有一定的规律性和相关性这一先验信息，通过将事件映射到极坐标时空邻域，建立事件的局部相关性，以此构建完整的概率图模型。同时，设计相应的能量函数将去噪问题转化为能量最小问题。此外，改进的条件迭代模式被用于优化模型的迭代求解。事件相机模拟器产生的仿真数据和DAVIS346录制的真实数据进行的去噪实验表明，该算法可有效地实现事件相机成像去噪。最后，通过和滤波算法进行对比，证明了该算法优于滤波算法。