1. HUE Dataset: High-Resolution Event and Frame Sequences for Low-Light Vision
- Author
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Ercan, Burak, Eker, Onur, Erdem, Aykut, and Erdem, Erkut
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
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., Comment: 18 pages, 4 figures. Has been accepted for publication at the European Conference on Computer Vision Workshops (ECCVW), Milano, 2024. The project page can be found at https://ercanburak.github.io/HUE.html
- Published
- 2024