Quantifying Noise of Dynamic Vision Sensor
This addresses noise quantification and denoising for dynamic vision sensors, which is an incremental improvement in sensor processing.
The paper tackled the problem of distinguishing noise from signal in dynamic vision sensors without ground truth by proposing a new technique based on Detrended Fluctuation Analysis to characterize background activity noise, and demonstrated its application for deriving optimal denoising filter parameters on a moving-car dataset.
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.