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Conventional digital cameras typically accumulate all the photons within an exposure period to form a snapshot image. It requires the scene to be quite still during the imaging time, otherwise it would result in blurry image for the moving objects. Recently, a retina-inspired spike camera has been proposed and shown great potential for recording high-speed motion scenes. Instead of capturing the visual scene by a single snapshot, the spike camera records the dynamic light intensity variation continuously. Each pixel on spike camera sensor accumulates the incoming photons independently and persistently, which fires a spike and restarts the photon accumulation immediately once the dispatch threshold is reached, producing a continuous stream of spikes recorded at very high temporal resolution. To recover the dynamic scene from captured spike stream, this paper presents an image reconstruction approach for spike camera. In order to generate high-quality reconstruction, we investigate the temporal correlation along motion trajectories and exploit it via adaptive temporal filtering. In particular, we present a hierarchical motion-aligned temporal filtering scheme, combining short-term filtering with long-term filtering to take advantage of long-term temporal correlation with low model complexity. Experimental results demonstrate that the proposed scheme outperforms the existing schemes significantly, producing much better objective and subjective qualities for spike camera image reconstruction.
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