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<p>The accurate recording of high-quality underwater cultural heritage images is crucial for archeological research. However, underwater images frequently suffer from color distortion and reduced clarity, which compromises image quality. Existing underwater image enhancement methods often lead to either over-enhancement or under-enhancement, thereby obscuring artifact details and hindering archeological research. This study proposes a method for enhancing quality and restoring color in underwater cultural heritage images, based on an underwater physical imaging model. First, a different background light estimation algorithm based on brightness segmentation (DBE-BS) is developed to enable adaptive multi-region background light estimation, thereby mitigating the impact of uneven lighting on the image. Next, the depth-saturation fusion transmission (DSFT) map estimation algorithm integrates depth information with the inverse saturation map, improving transmission map accuracy. Finally, the Depth-integrated Color Compensation Model (DICC) is introduced to optimize color correction using image depth data, enhancing the image’s visual quality.</p>

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Underwater heritage image enhancement and color restoration integrating partitioned background light estimation and deep fusion

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月4日

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  • Dongwei Qiu,
  • Tiancheng Liu,
  • Ji Wang,
  • Shanshan Wan

摘要

The accurate recording of high-quality underwater cultural heritage images is crucial for archeological research. However, underwater images frequently suffer from color distortion and reduced clarity, which compromises image quality. Existing underwater image enhancement methods often lead to either over-enhancement or under-enhancement, thereby obscuring artifact details and hindering archeological research. This study proposes a method for enhancing quality and restoring color in underwater cultural heritage images, based on an underwater physical imaging model. First, a different background light estimation algorithm based on brightness segmentation (DBE-BS) is developed to enable adaptive multi-region background light estimation, thereby mitigating the impact of uneven lighting on the image. Next, the depth-saturation fusion transmission (DSFT) map estimation algorithm integrates depth information with the inverse saturation map, improving transmission map accuracy. Finally, the Depth-integrated Color Compensation Model (DICC) is introduced to optimize color correction using image depth data, enhancing the image’s visual quality.