首个高分辨率事件与帧序列数据集，助力低光视觉研究
HUE Dataset: High-Resolution Event and Frame Sequences for Low-Light Vision
- 构建了106个低光场景的高分辨率事件与帧同步数据
- 验证现有方法在无参考指标下表现，发现事件法易误检
- 适合低光增强、事件相机、多模态视觉研究者使用
低光环境给图像增强带来巨大挑战。为此，本文提出HUE数据集，包含106个序列，覆盖室内、城市、黄昏、夜间、驾驶及受控场景，全面涵盖不同光照水平与动态范围。采用混合RGB与事件相机系统，采集高分辨率事件数据与互补帧数据。通过无参考指标对前沿低光增强与事件图像重建方法进行定性定量评估，并在下游目标检测任务上测试性能。结果表明，尽管事件方法在特定指标上表现优异，但在实际应用中可能产生误报。该数据集与分析为低光视觉与混合相机系统研究提供重要参考。
原文摘要 · Abstract (English)
Low-light environments pose significant challenges for image enhancement methods. To address these challenges, in this work, we introduce the HUE dataset, a comprehensive collection of high-resolution event and frame sequences captured in diverse and challenging low-light conditions. Our dataset includes 106 sequences, encompassing indoor, cityscape, twilight, night, driving, and controlled scenarios, each carefully recorded to address various illumination levels and dynamic ranges. Utilizing a hybrid RGB and event camera setup. we collect a dataset that combines high-resolution event data with complementary frame data. We employ both qualitative and quantitative evaluations using no-reference metrics to assess state-of-the-art low-light enhancement and event-based image reconstruction methods. Additionally, we evaluate these methods on a downstream object detection task. Our findings reveal that while event-based methods perform well in specific metrics, they may produce false positives in practical applications. This dataset and our comprehensive analysis provide valuable insights for future research in low-light vision and hybrid camera systems.
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