{
  "id": "2404.03210",
  "version": "v1",
  "published": "2024-04-04T05:33:06.000Z",
  "updated": "2024-04-04T05:33:06.000Z",
  "title": "HDR Imaging for Dynamic Scenes with Events",
  "authors": [
    "Li Xiaopeng",
    "Zeng Zhaoyuan",
    "Fan Cien",
    "Zhao Chen",
    "Deng Lei",
    "Yu Lei"
  ],
  "categories": [
    "cs.CV",
    "eess.IV"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-04-04T05:33:06.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "dynamic scenes",
      "high dynamic range",
      "hdr imaging",
      "ground-truth sharp hdr images aggravate",
      "unavailable ground-truth sharp hdr images"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}