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

• URL: http://arxiv.org/abs/2607.06217v1
• Date: Tue, 07 Jul 2026 12:41:36 GMT
• Title: EeveeDark: A Binary Neural Framework for Low-Light Video Enhancement via Event-Guided Sensor-Level Fusion
• Abstract summary: This paper introduces NeuralDark, a low-light video enhancement framework that combines the richness of sensor-level RAW data with the temporal precision of event streams.<n>Central to our model is a Binary Network (BNN) architecture that reduces computational overhead by quantizing weights and activations.
• Score: 20.168125532652812
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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