Abstract
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.
Highlights
The rapid development of imaging sensors has caused a geometric increase in the image-data quantity, rendering it difficult for the current algorithms and computing power to process such massive image data rapidly
We present a method based for reducing thespatiotemporal noise in the eventcorrelation: stream of a Dynamic vision sensor (DVS), baseda piece of for quantifying image quality is proposed on the model on the event density in the spatiotemporal neighborhood
We propose a method for denoising the DVS output event stream based on event density and a method for evaluating filter performance without the need for a fixed pattern generator
Summary
The rapid development of imaging sensors has caused a geometric increase in the image-data quantity, rendering it difficult for the current algorithms and computing power to process such massive image data rapidly. Dynamic vision sensor (DVS) can solve the problem of large image-data quantities, from a hardware perspective [1,2,3]. In the case of fast moving targets and large dynamic range, DVS can achieve low power visual sensing, but it is a challenge to traditional detectors [4]. The output of events is asynchronous, rather than the synchronous output of traditional sensors in frame. Such characteristics make DVS more advantageous in areas such as moving target detection, simultaneous localization and mapping (SLAM), and drones [6,7,8,9,10,11,12,13]. Commercial companies have used them for automotive and other fields [14,15]
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