论文标题
与单光子摄像机运动自适应脱毛
Motion Adaptive Deblurring with Single-Photon Cameras
论文作者
论文摘要
单光子雪崩二极管（SPADS）是一种具有极端低光灵敏度和平时定时分辨率的快速发展的图像传感技术。这些独特的功能使Spads可以用于LIDAR，非线视觉成像和荧光显微镜等应用中，这些光线需要在光子含有的场景中进行成像。在这项工作中，我们利用这些功能在低照明条件下的被动成像环境中处理运动模糊。我们的关键见解是，Spad阵列摄像机捕获的数据可以表示为光子检测事件的3D时空张量，可以根据场景运动进行动态变化的集成窗口沿任意时空轨迹集成，并具有动态变化的集成窗口。我们提出了一种算法，该算法从光子时间戳数据估算像素运动，并动态调整积分窗口以最大程度地减少运动模糊。我们的仿真结果显示了该算法对各种运动曲线的适用性，包括翻译，旋转和局部对象运动。我们还证明了我们方法对使用32x32 SPAD摄像机捕获的数据的现实可行性。
Single-photon avalanche diodes (SPADs) are a rapidly developing image sensing technology with extreme low-light sensitivity and picosecond timing resolution. These unique capabilities have enabled SPADs to be used in applications like LiDAR, non-line-of-sight imaging and fluorescence microscopy that require imaging in photon-starved scenarios. In this work we harness these capabilities for dealing with motion blur in a passive imaging setting in low illumination conditions. Our key insight is that the data captured by a SPAD array camera can be represented as a 3D spatio-temporal tensor of photon detection events which can be integrated along arbitrary spatio-temporal trajectories with dynamically varying integration windows, depending on scene motion. We propose an algorithm that estimates pixel motion from photon timestamp data and dynamically adapts the integration windows to minimize motion blur. Our simulation results show the applicability of this algorithm to a variety of motion profiles including translation, rotation and local object motion. We also demonstrate the real-world feasibility of our method on data captured using a 32x32 SPAD camera.
