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Open AccessArticle
Event Stream Denoising Method Based on Spatio-Temporal Density and Time Sequence Analysis
by
Haiyan Jiang
Haiyan Jiang ,
Xiaoshuang Wang
Xiaoshuang Wang ,
Wei Tang
Wei Tang ,
Qinghui Song
Qinghui Song ,
Qingjun Song
Qingjun Song * and
Wenchao Hao
Wenchao Hao
College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271000, China
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(20), 6527; https://doi.org/10.3390/s24206527 (registering DOI)
Submission received: 5 September 2024
/
Revised: 30 September 2024
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Accepted: 9 October 2024
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Published: 10 October 2024
Abstract
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.
Share and Cite
MDPI and ACS Style
Jiang, H.; Wang, X.; Tang, W.; Song, Q.; Song, Q.; Hao, W.
Event Stream Denoising Method Based on Spatio-Temporal Density and Time Sequence Analysis. Sensors 2024, 24, 6527.
https://doi.org/10.3390/s24206527
AMA Style
Jiang H, Wang X, Tang W, Song Q, Song Q, Hao W.
Event Stream Denoising Method Based on Spatio-Temporal Density and Time Sequence Analysis. Sensors. 2024; 24(20):6527.
https://doi.org/10.3390/s24206527
Chicago/Turabian Style
Jiang, Haiyan, Xiaoshuang Wang, Wei Tang, Qinghui Song, Qingjun Song, and Wenchao Hao.
2024. "Event Stream Denoising Method Based on Spatio-Temporal Density and Time Sequence Analysis" Sensors 24, no. 20: 6527.
https://doi.org/10.3390/s24206527
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