EeveeDark: A Binary Neural Framework for Low-Light Video Enhancement via Event-Guided Sensor-Level Fusion

2026-07-07

AI Summary

This work addresses the challenge of achieving high-quality video enhancement under extreme low-light conditions while maintaining computational efficiency in resource-constrained settings. To this end, it introduces binary neural networks (BNNs) into RAW-event multimodal fusion for the first time, proposing a modality-specific binary encoder, a lightweight cross-modal fusion module, and an event-guided skip gating mechanism to enable dynamic spatiotemporal optimization. Evaluated on both synthetic and real-world low-light datasets, the proposed method significantly outperforms existing BNN-based approaches, delivering superior enhancement quality while substantially reducing computational overhead. This approach effectively strikes a balance between performance and efficiency, making it particularly suitable for practical deployment in low-power or embedded vision systems.

Abstract

Enhancing videos under extreme low-light conditions remains challenging due to the difficulty of balancing restoration quality and computational efficiency in resource-constrained settings. This paper introduces EeveeDark, a low-light video enhancement framework that combines the spatial richness of sensor-level RAW data with the temporal precision of event streams. Central to our model is a Binary Neural Network (BNN) architecture that reduces computational overhead by quantizing weights and activations while preserving detail. EeveeDark incorporates (i) modality-specific binary encoders for processing RAW frames and event data, (ii) a lightweight fusion block for integrating spatial and temporal cues, and (iii) an event-guided skip gating mechanism for dynamic spatiotemporal refinement. Experiments on synthetic and real-world datasets show that EeveeDark outperforms prior BNN-based methods and offers a favorable performance-efficiency trade-off compared to full-precision models. The project page is available at https://cyberiada.github.io/EeveeDark.

Problem

• low-light video enhancement
• computational efficiency
• resource-constrained settings
• spatiotemporal restoration

Innovation

• Binary Neural Network
• Event-Guided Fusion
• Low-Light Video Enhancement
• Sensor-Level RAW
• Spatiotemporal Refinement