Reconstruct Dynamic Scene for Spike Camera Based on 3D Space Time Similarity

Spike camera is a neuromorphic camera that recurrently accumulates photons and fires spikes to record the incident light intensity at very high temporal resolution, making it particularly suitable for recording high dynamic scenes. This paper addresses the problem of image reconstruction for spike camera. Due to the Poisson effect of photon arrival and the quantization effect of spike readout, the spike interval calculated from a single spike cycle cannot reflect the light intensity accurately. Firstly, this paper analyzes the error of spike interval estimation under static light intensity. Then, this paper focuses on the temporal correlation of continuous spikes under dynamic light intensity. Specifically, it considers 3D space time similarity to weighted average multiple continuous spike intervals for the intensity estimation at a certain pixel. Experimental results demonstrate that the proposed method achieves better performance in both objective and subjective aspects compared with previous reconstruction methods.