Super-Resolution Reconstruction from Bayer-Pattern Spike Streams

Yanchen Dong, Ruiqin Xiong, Jian Zhang, Zhaofei Yu, Xiaopeng Fan, Shuyuan Zhu, Tiejun Huang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 24871-24880

Spike camera is a neuromorphic vision sensor that can capture highly dynamic scenes by generating a continuous stream of binary spikes to represent the arrival of photons at very high temporal resolution. Equipped with Bayer color filter array (CFA) color spike camera (CSC) has been invented to capture color information. Although spike camera has already demonstrated great potential for high-speed imaging its spatial resolution is limited compared with conventional digital cameras. This paper proposes a Color Spike Camera Super-Resolution (CSCSR) network to super-resolve higher-resolution color images from spike camera streams with Bayer CFA. To be specific we first propose a representation for Bayer-pattern spike streams exploring local temporal information with global perception to represent the binary data. Then we exploit the CFA layout and sub-pixel level motion to collect temporal pixels for the spatial super-resolution of each color channel. In particular a residual-based module for feature refinement is developed to reduce the impact of motion estimation errors. Considering color correlation we jointly utilize the multi-stage temporal-pixel features of color channels to reconstruct the high-resolution color image. Experimental results demonstrate that the proposed scheme can reconstruct satisfactory color images with both high temporal and spatial resolution from low-resolution Bayer-pattern spike streams. The source codes are available at https://github.com/csycdong/CSCSR.