HUE Dataset: High-Resolution Event and Frame Sequences for Low-Light Vision

• URL: http://arxiv.org/abs/2410.19164v1
• Date: Thu, 24 Oct 2024 21:15:15 GMT
• Title: HUE Dataset: High-Resolution Event and Frame Sequences for Low-Light Vision
• Authors: Burak Ercan, Onur Eker, Aykut Erdem, Erkut Erdem,
• Abstract summary: We introduce the HUE dataset, a collection of high-resolution event and frame sequences captured in low-light conditions. Our dataset includes 106 sequences, encompassing indoor, cityscape, twilight, night, driving, and controlled scenarios. We employ both qualitative and quantitative evaluations to assess state-of-the-art low-light enhancement and event-based image reconstruction methods.
• Score: 16.432164340779266
• License:
• 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.

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