Quantifying Noise of Dynamic Vision Sensor
Evgeny V. Votyakov Alessandro Artusi
Dynamic visual sensors (DVS) are characterized by a large amount of
background activity (BA) noise, which it is mixed with the original (cleaned)
sensor signal. The dynamic nature of the signal and the absence in practical
application of the ground truth, it clearly makes difficult to distinguish
between noise and the cleaned sensor signals using standard image processing
techniques. In this letter, a new technique is presented to characterise BA
noise derived from the Detrended Fluctuation Analysis (DFA). The proposed
technique can be used to address an existing DVS issues, which is how to
quantitatively characterised noise and signal without ground truth, and how to
derive an optimal denoising filter parameters. The solution of the latter
problem is demonstrated for the popular real moving-car dataset.