Spike Signal Reconstruction Based on Inter-Spike Similarity

Abstract

Spike camera is a kind of bio-inspired camera which is particularly proposed for capturing dynamic scenes with high speed motion. Spike camera works in a way simulating the retina that it receives incoming photons continuously and fires a spike whenever the accumulated photons reach a threshold. The spike stream can be recorded at an extremely high temporal resolution so that the dynamic process of light-intensity changes may be recovered. This paper addresses the problem of recovering the original visual signal from spikes. To reduce the fluctuation in spike intervals caused by the Poisson effect of photon arrivals and the quantization effect in spike reading, we estimate the real interval from a sequence of temporally neighboring spikes. To avoid mixing the spikes generated from significantly different light intensities, we propose a temporal and spatial weighting method based on the inter-spike similarity. Experimental results demonstrate that the proposed method outperforms the previous light intensity inference methods and achieves better performance under different motion conditions.