EeveeDark: A Binary Neural Framework for Low-Light Video Enhancement via Event-Guided Sensor-Level Fusion
IEEE Robotics and Automation Letters, vol.11, no.4, pp.4633-4640, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 11 Issue: 4
- Publication Date: 2026
- Doi Number: 10.1109/lra.2026.3666388
- Journal Name: IEEE Robotics and Automation Letters
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Page Numbers: pp.4633-4640
- Keywords: deep learning for visual perception, event camera, low-light video enhancement, Sensor fusion
- Hacettepe University Affiliated: Yes
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.