Event Density Based Denoising Method for Dynamic Vision Sensor

Dynamic vision sensor (DVS) is a new type of image sensor, which has application prospects in the fields of automobiles and robots. Dynamic vision sensors are very different from traditional image sensors in terms of pixel principle and output data. Background activity (BA) in the data will affect i...

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Bibliographic Details
Published inApplied sciences Vol. 10; no. 6; p. 2024
Main Authors Feng, Yang, Lv, Hengyi, Liu, Hailong, Zhang, Yisa, Xiao, Yuyao, Han, Chengshan
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2020
Subjects
Algorithms
Computer applications
Data transmission
denoise
Design
dynamic vision sensor
Field programmable gate arrays
filter performance evaluation
Image filters
Image quality
Methods
Neighborhoods
Noise
Noise reduction
Pattern generation
Quality
Sensors
Spacetime
Vision
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Summary:Dynamic vision sensor (DVS) is a new type of image sensor, which has application prospects in the fields of automobiles and robots. Dynamic vision sensors are very different from traditional image sensors in terms of pixel principle and output data. Background activity (BA) in the data will affect image quality, but there is currently no unified indicator to evaluate the image quality of event streams. This paper proposes a method to eliminate background activity, and proposes a method and performance index for evaluating filter performance: noise in real (NIR) and real in noise (RIN). The lower the value, the better the filter. This evaluation method does not require fixed pattern generation equipment, and can also evaluate filter performance using natural images. Through comparative experiments of the three filters, the comprehensive performance of the method in this paper is optimal. This method reduces the bandwidth required for DVS data transmission, reduces the computational cost of target extraction, and provides the possibility for the application of DVS in more fields.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10062024