HDR Imaging for Dynamic Scenes with Events
<|reference_start|>HDR Imaging for Dynamic Scenes with Events: High dynamic range imaging (HDRI) for real-world dynamic scenes is challenging because moving objects may lead to hybrid degradation of low dynamic range and motion blur. Existing event-based approaches only focus on a separate task, while cascading HDRI and motion deblurring would lead to sub-optimal solutions, and unavailable ground-truth sharp HDR images aggravate the predicament. To address these challenges, we propose an Event-based HDRI framework within a Self-supervised learning paradigm, i.e., Self-EHDRI, which generalizes HDRI performance in real-world dynamic scenarios. Specifically, a self-supervised learning strategy is carried out by learning cross-domain conversions from blurry LDR images to sharp LDR images, which enables sharp HDR images to be accessible in the intermediate process even though ground-truth sharp HDR images are missing. Then, we formulate the event-based HDRI and motion deblurring model and conduct a unified network to recover the intermediate sharp HDR results, where both the high dynamic range and high temporal resolution of events are leveraged simultaneously for compensation. We construct large-scale synthetic and real-world datasets to evaluate the effectiveness of our method. Comprehensive experiments demonstrate that the proposed Self-EHDRI outperforms state-of-the-art approaches by a large margin. The codes, datasets, and results are available at https://lxp-whu.github.io/Self-EHDRI.<|reference_end|>
@article{xiaopeng2024hdr,
    title={HDR Imaging for Dynamic Scenes with Events},
    author={Li Xiaopeng, Zeng Zhaoyuan, Fan Cien, Zhao Chen, Deng Lei, Yu Lei},
    journal={arXiv preprint arXiv:2404.03210},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03210},
    primaryClass={cs.CV eess.IV}
}