Spike Camera Image Reconstruction Using Deep Spiking Neural Networks

Abstract

Spike camera is a bio-inspired sensor with ultra-high temporal resolution and low energy consumption. It captures visual signals using an "integrate-and-fire" mechanism and outputs a continuous stream of binary spikes. Reconstructing image sequence from spikes streams is critical for spike camera. Several reconstruction methods have been proposed in recent years. However, the computational cost of these methods is relatively high. Inspired by the fact that spiking neural networks (SNNs) are energy efficient and support time-series signal processing inherently, we propose a lightweight SNN for spike camera image reconstruction (abbreviated to SSIR). Experimental results show that SSIR achieves comparable performance with the state-of-the-art (SOTA) methods at much lower computation and energy cost.