A new sparse-satellite event-camera benchmark with ground truth shows learning-based FEAST is the most accurate noise filter, followed by EvFlow for signal preservation.
Optimal biasing and physical limits of DVS event noise
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abstract
Under dim lighting conditions, the output of Dynamic Vision Sensor (DVS) event cameras is strongly affected by noise. Photon and electron shot-noise cause a high rate of non-informative events that reduce Signal to Noise ratio. DVS noise performance depends not only on the scene illumination, but also on the user-controllable biasing of the camera. In this paper, we explore the physical limits of DVS noise, showing that the DVS photoreceptor is limited to a theoretical minimum of 2x photon shot noise, and we discuss how biasing the DVS with high photoreceptor bias and adequate source-follower bias approaches optimal noise performance. We support our conclusions with pixel-level measurements of a DAVIS346 and analysis of a theoretical pixel model.
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cs.CV 1years
2024 1verdicts
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Noise Filtering Benchmark for Neuromorphic Satellites Observations
A new sparse-satellite event-camera benchmark with ground truth shows learning-based FEAST is the most accurate noise filter, followed by EvFlow for signal preservation.