Hacettepe University Event (HUE) Dataset - Indoor - Part 2

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.低光照环境对图像增强方法而言是极具挑战性的应用场景。为应对此类挑战，本研究提出HUE数据集（HUE dataset）——一个采集自多样严苛低光照条件下的高分辨率事件序列与帧序列综合集合。本数据集共包含106段序列，涵盖室内、城市场景、暮光环境、夜间、驾驶场景以及受控实验场景，每段序列均经过精心录制，覆盖不同光照水平与动态范围。我们采用RGB与事件相机（event camera）混合采集方案，构建了兼具高分辨率事件数据与互补帧数据的数据集。我们通过无参考评估指标，采用定性与定量相结合的方式，对当前前沿的低光照增强及基于事件的图像重建方法进行性能测评。此外，我们还在下游目标检测任务中对上述方法展开评估。研究结果显示，尽管基于事件的方法在部分指标上表现出色，但在实际应用中仍可能产生假阳性检测结果。本数据集与本次全面分析可为低光照视觉及混合相机系统领域的未来研究提供宝贵的参考与启示。