基于改进马尔可夫随机场的事件相机去噪算法

Denoising algorithm based on improved Markov random field for event camera

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。

To solve the problem of the large amount of noise in the event stream output by the event camera, an event stream denoising algorithm based on the probability undirected graph model was introduced. Due to the imaging principle of the camera, the change of the target had certain regularity and correlation in time and space.By mapping the event to the polar coordinate space-time neighborhood, the local correlation of the event was established to build a complete probability graph model. In addition, the improved conditional iterative mode algorithm was used to optimize the iterative solution of model. The experimental results of simulated data generated by the event camera simulator and the real data recorded by DAVIS346 show that the proposed algorithm can effectively remove noise events. Finally, the comparison with the filtering algorithm proves that the algorithm is superior to the filtering algorithm.