SpikeODE: Image Reconstruction for Spike Camera With Neural Ordinary Differential Equation

The recently invented retina-inspired spike camera has shown great potential for capturing dynamic scenes. However, reconstructing high-quality images from the binary spike data remains a challenge due to the existence of noises in the camera. This paper proposes SpikeODE, a novel approach to reconstructing clear images by exploring temporal-spatial correlation to depress noises. The main idea of our method is to restore the continuous dynamic process of real scenes in a latent space and learn the temporal correlations in a fine-grained manner. Furthermore, to model the dynamic process more effectively, we design a conditional ODE where the latent state of each timestamp is conditioned on the observed spike data. Subsequently, forward and backward inferences are conducted through the ODE to investigate the correlations between the representation of the target timestamp and the information from both past and future contexts. Additionally, we incorporate a Unet structure with a pixel-wise attention mechanism at each level to learn spatial correlations. Experimental results demonstrate that our method outperforms state-of-the-art methods across several metrics.