提出新指标PCD，量化浑浊水下场景的信息损失。
Beyond Aesthetics: Quantifying Information Loss in Turbid Scenes
- 基于相位一致性构建抗对比度干扰的结构信息损失度量PCD。
- PCD与实例分割模型性能高度相关，而传统指标无关。
- 公开1320张真实浑浊水下图像与超1.6万标注掩膜数据集。
水下环境在浑浊条件下可视性迅速下降，但其对计算机视觉模型的影响尚不明确。现有研究依赖合成浑浊数据集，可能无法真实反映信息丢失情况。为此，我们提出浑浊水下基准数据集TUB，包含1,320张极端浑浊条件下拍摄的图像及超过16,000个高置信度真值分割掩膜。我们还提出一种名为PCD的度量方法，基于相位一致性图，对对比度不变，旨在捕捉真实浑浊条件下的结构信息损失。实验表明，PCD在真实与合成浑浊图像上均与实例分割模型性能高度相关，而领域内常用指标则表现出弱相关甚至无相关。相关数据集与代码已发布于项目页面：https://vap.aau.dk/pcd。
原文摘要 · Abstract (English)
Visibility in underwater environments degrades rapidly under turbid conditions, yet the effects on computer-vision models remain unclear. This issue is compounded by reliance on synthetic turbidity datasets, which may misrepresent real-world information loss. To address this gap, we introduce the Turbid Underwater Baseline (TUB) dataset, comprising 1,320 images captured under extreme turbidity and over 16,000 high-confidence ground-truth segmentation masks. We additionally propose PCD, a metric derived from phase congruency maps that is invariant to contrast and aims to capture the loss of structural information in real turbidity. We show that PCD correlates strongly with the performance of instance segmentation models on both real and synthetic turbid images, whereas common metrics in the field show weak to no correlation at all. The dataset and relevant code can be found on the project page: https://vap.aau.dk/pcd
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