Jiang Haiyan, Wang Xiaoshuang, Tang Wei, Song Qinghui, Song Qingjun, Hao Wenchao
College of Intelligent Equipment, Shandong University of Science and Technology, Tai'an 271000, China.
Sensors (Basel). 2024 Oct 10;24(20):6527. doi: 10.3390/s24206527.
An event camera is a neuromimetic sensor inspired by the human retinal imaging principle, which has the advantages of high dynamic range, high temporal resolution, and low power consumption. Due to the interference of hardware and software and other factors, the event stream output from the event camera usually contains a large amount of noise, and traditional denoising algorithms cannot be applied to the event stream. To better deal with different kinds of noise and enhance the robustness of the denoising algorithm, based on the spatio-temporal distribution characteristics of effective events and noise, an event stream noise reduction and visualization algorithm is proposed. The event stream enters fine filtering after filtering the BA noise based on spatio-temporal density. The fine filtering performs time sequence analysis on the event pixels and the neighboring pixels to filter out hot noise. The proposed visualization algorithm adaptively overlaps the events of the previous frame according to the event density difference to obtain clear and coherent event frames. We conducted denoising and visualization experiments on real scenes and public datasets, respectively, and the experiments show that our algorithm is effective in filtering noise and obtaining clear and coherent event frames under different event stream densities and noise backgrounds.
事件相机是一种受人类视网膜成像原理启发的神经拟态传感器，具有高动态范围、高时间分辨率和低功耗等优点。由于硬件和软件等因素的干扰，事件相机输出的事件流通常包含大量噪声，传统的去噪算法无法应用于事件流。为了更好地处理各种噪声并增强去噪算法的鲁棒性，基于有效事件和噪声的时空分布特性，提出了一种事件流降噪与可视化算法。事件流在基于时空密度过滤BA噪声后进入精细过滤。精细过滤对事件像素和相邻像素进行时间序列分析以滤除热噪声。所提出的可视化算法根据事件密度差异自适应地叠加前一帧的事件，以获得清晰连贯的事件帧。我们分别在真实场景和公共数据集上进行了去噪和可视化实验，实验表明我们的算法在不同事件流密度和噪声背景下对过滤噪声和获得清晰连贯的事件帧是有效的。
