EvRWKV: A RWKV Framework for Effective Event-guided Low-Light Image Enhancement

WenJie Cai, Qingguo Meng, Zhenyu Wang, Xingbo Dong, Zhe Jin

Abstract: Capturing high-quality visual content under low-light conditions remains a challenging problem due to severe noise and underexposure, which degrade the performance of downstream applications. Traditional frame-based low-light image enhancement methods often amplify noise or fail to preserve structural details. Event cameras, offering high dynamic range and microsecond temporal resolution by asynchronously capturing brightness changes, emerge as a promising complement for low-light imaging. However, existing fusion methods fail to fully exploit this synergy, either by forcing modalities into a shared representation too early or by losing vital low-level correlations through isolated processing. To address these challenges, we propose EvRWKV, a novel framework that enables continuous cross-modal interaction through dual-domain processing. Our approach incorporates a Cross-RWKV module, leveraging the Receptance Weighted Key Value (RWKV) architecture for fine-grained temporal and cross-modal fusion, and an Event Image Spectral Fusion Enhancer (EISFE) module, which jointly performs adaptive frequency-domain noise suppression and spatial-domain deformable convolution alignment. This continuous interaction maintains feature consistency from low-level textures to high-level semantics. Extensive qualitative and quantitative evaluations on real-world low-light datasets (SDE, SDSD, RELED) demonstrate that EvRWKV achieves state-of-the-art performance, effectively enhancing image quality by suppressing noise, restoring structural details, and improving visual clarity in challenging low-light conditions.