基于突出区域识别的Denoising方法用于时空事件流的时空空间事件流
Sichao Tang1,2, Hengyi Lv1, Yuchen Zhao1
1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究引入了一种用于事件摄像机 (动态视觉传感器) 的新消噪方法,该方法可以有效地消除闪灯和反射的噪声. 新方法保留了关键事件数据,改善了感知和算法性能.
科学领域:
- 计算机视觉 计算机视觉
- 传感器技术 传感器技术
- 生物模拟学是一种生物模拟学.
背景情况:
- 事件摄像头或动态视觉传感器提供基于事件的高速数据捕获.
- 事件摄像头数据中的噪声,由于光敏度和外部因素,降低了感知和算法性能.
- 传统的图像否定方法不适合事件摄像头的地址事件表示输出.
研究的目的:
- 为事件摄像头数据开发一种有效的无声化方法.
- 解决现有方法在处理各种类型的噪音方面的局限性,包括来自闪灯和分散反射的噪音.
- 为了确保在消毒过程中保证真实事件的保存.
主要方法:
- 提出了一种使用突出区域识别的新型事件流拒绝方法.
- 开发了一个新的评估指标,以评估denoising有效性和真实事件保存.
- 对传统的背景活动噪声和不规则的噪声源进行了测试.
主要成果:
- 提出的方法成功地消除了来自分散反射和闪光源的背景活动噪声和不规则噪声.
- 该方法证明了对真实事件数据的最小损失.
- 新的评价指标提供了对销业绩的定量评估.
结论:
- 基于突出区域识别的无线化方法为事件摄像头数据处理提供了显著的改进.
- 这种方法提高了使用事件摄像头流的算法的可靠性和性能.
- 开发的评估指标有助于比较和推进事件摄像头除技术.
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