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High-Speed Motion Scene Reconstruction for Spike Camera via Motion Aligned Filtering

A new retina-inspired bionic spike camera has recently shown great potential for capturing high speed movements. Unlike conventional cameras with a fixed low sampling rate, retina-inspired spike camera can well record fast-moving scenes by continuously accumulating luminance intensity and firing spikes. To restore the captured high-speed motion scenes from spike data, several reconstruction methods have been proposed. A typical method utilizes two neighbouring spikes to infer the instantaneous luminance intensity. Although high temporal resolution imaging can be achieved, the signal to noise ratio (SNR) of reconstructions is generally unsatisfactory. For improving the SNR, some methods propose to average the spikes in big time window. However, the reconstructions may suffer from undesired motion blur, especially when there are objects moving very fast in scenes. To address this issue, we develop a new image reconstruction approach for potential retina-inspired spike camera to recover high-speed motion scenes. Specially, we take the motion of objects into consideration and exploit optical flow to align the scenes of different moments. After motion alignment, a filtering along motion trajectory can be employed to the signals to take the advantage of temporal correlations while not introducing undesired motion blur. Experimental results demonstrate that our proposed method achieves better visual quality than previous reconstruction schemes.