Learning a Deep Demosaicing Network for Spike Camera With Color Filter Array

For capturing dynamic scenes with ultra-fast motion, neuromorphic cameras with extremely high temporal resolution have demonstrated their great capability and potential. Different from the event cameras that only record relative changes in light intensity, spike camera fires a stream of spikes according to a full-time accumulation of photons so that it can recover the texture details for both static areas and dynamic areas. Recently, color spike camera has been invented to record color information of dynamic scenes using a color filter array (CFA). However, demosaicing for color spike cameras is an open and challenging problem. In this paper, we develop a demosaicing network, called CSpkNet, to reconstruct dynamic color visual signals from the spike stream captured by the color spike camera. Firstly, we develop a light inference module to convert binary spike streams to intensity estimates. In particular, a feature-based channel attention module is proposed to reduce the noises caused by quantization errors. Secondly, considering both the Bayer configuration and object motion, we propose a motion-guided filtering module to estimate the missing pixels of each color channel, without undesired motion blur. Finally, we design a refinement module to improve the intensity and details, utilizing the color correlation. Experimental results demonstrate that CSpkNet can reconstruct color images from the Bayer-pattern spike stream with promising visual quality.