HUE Dataset: High-Resolution Event and Frame Sequences for Low-Light Vision
This work provides a new dataset for researchers in low-light vision and hybrid camera systems, but it is incremental as it focuses on data collection and evaluation rather than novel methods.
The authors introduced the HUE dataset, a collection of 106 high-resolution event and frame sequences in diverse low-light conditions, to address challenges in low-light image enhancement. They found that event-based methods perform well in some metrics but may produce false positives in practical applications like object detection.
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.