Color Spike Camera Reconstruction via Long Short-Term Temporal Aggregation of Spike Signals

With the prevalence of emerging computer vision applications, the demand for capturing dynamic scenes with high-speed motion has increased. A kind of neuromorphic sensor called spike camera shows great potential in this aspect since it generates a stream of binary spikes to describe the dynamic light intensity with a very high temporal resolution. Color spike camera (CSC) was recently invented to capture the color information of dynamic scenes via a color filter array (CFA) on the sensor. This paper proposes a long short-term temporal aggregation strategy of spike signals. First, we utilize short-term temporal correlation to adaptively extract temporal features of each time point. Then we align the features and aggregate them to exploit long-term temporal correlation, suppressing undesired motion blur. To implement the strategy, we design a CSC reconstruction network. Based on adaptive short-term temporal aggregation, we propose a spike representation module to extract temporal features of each color channel, leveraging multiple temporal scales. Considering the long-term temporal correlation, we develop an alignment module to align the temporal features. In particular, we perform motion alignment of red and blue channels with the guidance of the higher-sampling-rate green channel, leveraging motion consistency among color channels. Besides, we propose a module to aggregate the aligned temporal features for the restored color image, which exploits color channel correlation. We have also developed a CSC simulator for data generation. Experimental results demonstrate that our method can restore color images with fine texture details, achieving state-of-the-art CSC reconstruction performance.