Interpolation-Based Event Visual Data Filtering Algorithms
This addresses noise reduction for neuromorphic vision applications, particularly in embedded devices, but appears incremental as it builds on existing filtering methods.
The paper tackles noise in event camera data streams by proposing four interpolation-based algorithms using infinite impulse response filters, achieving approximately 99% noise removal while preserving most valid signals and using about 30KB of memory for a 1280 x 720 resolution sensor.
The field of neuromorphic vision is developing rapidly, and event cameras are finding their way into more and more applications. However, the data stream from these sensors is characterised by significant noise. In this paper, we propose a method for event data that is capable of removing approximately 99\% of noise while preserving the majority of the valid signal. We have proposed four algorithms based on the matrix of infinite impulse response (IIR) filters method. We compared them on several event datasets that were further modified by adding artificially generated noise and noise recorded with dynamic vision sensor. The proposed methods use about 30KB of memory for a sensor with a resolution of 1280 x 720 and is therefore well suited for implementation in embedded devices.