Exploiting Alternating DVS Shot Noise Event Pair Statistics to Reduce Background Activity

TL;DR

This paper introduces a new understanding of shot noise event pair statistics in Dynamic Vision Sensors, leading to two biasing techniques that significantly reduce noise while maintaining sensor performance.

Contribution

It provides a fundamental analysis of shot noise event pairs in DVS and develops practical biasing methods to reduce noise by up to 80% without losing sensitivity.

Findings

Shot noise events form sequential opposite polarity pairs.

The proposed biasing techniques reduce shot noise by 50% and 80%.

Sensor sensitivity and temporal resolution are preserved.

Abstract

Dynamic Vision Sensors (DVS) record "events" corresponding to pixel-level brightness changes, resulting in data-efficient representation of a dynamic visual scene. As DVS expand into increasingly diverse applications, non-ideal behaviors in their output under extreme sensing conditions are important to consider. Under low illumination (below ~10 lux) their output begins to be dominated by shot noise events (SNEs) which increase the data output and obscure true signal. SNE rates can be controlled to some degree by tuning circuit parameters to reduce sensitivity or temporal response bandwidth at the cost of signal loss. Alternatively, an improved understanding of SNE statistics can be leveraged to develop novel techniques for minimizing uninformative sensor output. We first explain a fundamental observation about sequential pairing of opposite polarity SNEs based on pixel circuit logic and validate our theory using DVS recordings and simulations. Finally, we derive a practical result from this new understanding and demonstrate two novel biasing techniques to reduce SNEs by 50% and 80% respectively while still retaining sensitivity and/or temporal resolution.