Ge Zhou, Zhang Pei, Gao Yizhao, So Hayden K-H, Lam Edmund Y
Opt Express. 2022 Jan 17;30(2):2206-2218. doi: 10.1364/OE.444948.
Laser speckle imaging (LSI) is a powerful tool for motion analysis owing to the high sensitivity of laser speckles. Traditional LSI techniques rely on identifying changes from the sequential intensity speckle patterns, where each pixel performs synchronous measurements. However, a lot of redundant data of the static speckles without motion information in the scene will also be recorded, resulting in considerable resources consumption for data processing and storage. Moreover, the motion cues are inevitably lost during the "blind" time interval between successive frames. To tackle such challenges, we propose neuromorphic laser speckle imaging (NLSI) as an efficient alternative approach for motion analysis. Our method preserves the motion information while excluding the redundant data by exploring the use of the neuromorphic event sensor, which acquires only the relevant information of the moving parts and responds asynchronously with a much higher sampling rate. This neuromorphic data acquisition mechanism captures fast-moving objects on the order of microseconds. In the proposed NLSI method, the moving object is illuminated using a coherent light source, and the reflected high frequency laser speckle patterns are captured with a bare neuromorphic event sensor. We present the data processing strategy to analyze motion from event-based laser speckles, and the experimental results demonstrate the feasibility of our method at different motion speeds.
由于激光散斑具有高灵敏度，激光散斑成像（LSI）是一种用于运动分析的强大工具。传统的LSI技术依赖于从连续的强度散斑图案中识别变化，其中每个像素进行同步测量。然而，场景中没有运动信息的静态散斑的大量冗余数据也会被记录下来，导致数据处理和存储消耗大量资源。此外，运动线索在连续帧之间的“盲”时间间隔内不可避免地会丢失。为了应对这些挑战，我们提出神经形态激光散斑成像（NLSI）作为一种高效的运动分析替代方法。我们的方法通过探索使用神经形态事件传感器来保留运动信息，同时排除冗余数据，该传感器仅获取运动部件的相关信息，并以高得多的采样率异步响应。这种神经形态数据采集机制能在微秒量级上捕获快速移动的物体。在所提出的NLSI方法中，使用相干光源照射移动物体，并用裸神经形态事件传感器捕获反射的高频激光散斑图案。我们提出了从基于事件的激光散斑分析运动的数据处理策略，实验结果证明了我们的方法在不同运动速度下的可行性。
