Coarse-To-Fine Spatio-Temporal Luminance-Aware Reconstruction For High-Speed Motion Scene

The continuous emission of spike stream offers more significant advantages over traditional fixed low sampling rate cameras. Although many reconstruction methods from spike streams have been proposed, the quality of recovered images remains suboptimal. Coarse-to-fine high-speed motion scene reconstruction reconstructs the spike sequence by dividing the dynamic and static regions. However, issues of limited texture richness and low contrasts are unsolved during the reconstruction of static spike. To address these issues, we propose a Coarse-to-Fine spatio-temporal Luminance-Aware Reconstruction (CFLAR) framework. Specially, we propose an adaptive luminance-aware reconstruction in spatio-temporal domain. To be specific, in the spatial domain, we perform region division and region merging of spike sequences based on luminance information, while non-uniform quantization of luminance information is mainly achieved through binary division and adaptive parameters division. In the temporal domain, we integrate alterable window length into the texture from playback to propose adaptive time shift window reconstruction, which enables to obtain reconstructed images with richer textures and higher contrasts. Experimental results demonstrate that our CFLAR method outperforms state-of-the-art approaches in terms of objective and subjective quality.