Robust Noise Modeling for Spike Camera via Time-Interval Quantification and Spike-DSLR Multimodal Dataset in Low-Light Imaging

The inherent differences between spike cameras and traditional frame-based cameras lead to more complex and diverse noise characteristics, particularly under extremely low-light conditions. Existing noise modeling approaches for spike camera predominantly rely on inter-spike intervals (ISI) for noise quantification, which often results in inaccurate noise characterization. Moreover, current datasets for spike camera image reconstruction tasks are either synthetic or lack corresponding high-quality reference images, severely limiting rigorous evaluation of noise modeling methods. To address this limitation, we propose a multimodal noise modeling framework for spike camera that integrates insights from traditional frame-based imaging into spike imaging. Specifically, we introduce a time-interval-based quantification method inspired by the exposure-time concept used in traditional frame-based cameras, enabling accurate noise characterization for spike camera. Furthermore, we present the Spike-DSLR Multimodal Dataset (SDMD), the first real-world dataset capturing aligned multimodal data pairs from spike cameras and Digital Single-Lens Reflex (DSLR) cameras, explicitly designed for evaluating spike camera noise models. Experimental results on SDMD demonstrate that our noise modeling approach significantly enhances spike camera image reconstruction quality under low-light conditions, achieving more than 1.6 dB improvement in PSNR compared to existing state-of-the-art methods. This validates both the necessity and effectiveness of adopting a multimodal perspective in spike camera noise modeling.