用事件相机提升暗光图像，连续交互更准更稳。
EvRWKV: A Continuous Interactive RWKV Framework for Effective Event-Guided Low-Light Image Enhancement
- 双域处理实现图像与事件的持续交互
- 实测比纯图像方法提升1.79~1.85 dB PSNR
- 适合需要高精度图像增强的下游任务
事件相机在暗光图像增强（LLIE）中具有巨大潜力，但现有融合方法面临根本矛盾：早期融合难以应对模态异质性，晚期融合则割裂关键特征关联。为此，我们提出EvRWKV框架，通过双域处理实现连续跨模态交互，包含用于捕捉细粒度时空与跨模态依赖的Cross-RWKV模块，以及用于联合自适应频域去噪与空域对齐的事件图像频谱融合增强器（EISFE）。该机制保持了从低层纹理到高层语义的特征一致性。在真实世界SDE和SDSD数据集上的大量实验表明，EvRWKV分别比仅使用图像的方法提升1.79 dB和1.85 dB的PSNR。为进一步验证其实际应用价值，我们评估了其对语义分割的影响，结果表明，经EvRWKV增强的图像使mIoU提升35.44%。
原文摘要 · Abstract (English)
Event cameras offer significant potential for Low-light Image Enhancement (LLIE), yet existing fusion approaches are constrained by a fundamental dilemma: early fusion struggles with modality heterogeneity, while late fusion severs crucial feature correlations. To address these limitations, we propose EvRWKV, a novel framework that enables continuous cross-modal interaction through dual-domain processing, which mainly includes a Cross-RWKV Module to capture fine-grained temporal and cross-modal dependencies, and an Event Image Spectral Fusion Enhancer (EISFE) module to perform joint adaptive frequency-domain denoising and spatial-domain alignment. This continuous interaction maintains feature consistency from low-level textures to high-level semantics. Extensive experiments on the real-world SDE and SDSD datasets demonstrate that EvRWKV significantly outperforms only image-based methods by 1.79 dB and 1.85 dB in PSNR, respectively. To further validate the practical utility of our method for downstream applications, we evaluated its impact on semantic segmentation. Experiments demonstrate that images enhanced by EvRWKV lead to a significant 35.44% improvement in mIoU.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。