Beyond Aesthetics: Quantifying Information Loss in Turbid Scenes
Researchers introduce the Turbid Underwater Baseline (TUB) dataset and a new metric, PCD, to quantify information loss in underwater scenes with extreme turbidity. PCD correlates strongly with instance segmentation performance, outperforming common metrics.
[2606.26295] Beyond Aesthetics: Quantifying Information Loss in Turbid Scenes
[Submitted on 24 Jun 2026]
Title:Beyond Aesthetics: Quantifying Information Loss in Turbid Scenes
View a PDF of the paper titled Beyond Aesthetics: Quantifying Information Loss in Turbid Scenes, by Vasiliki Ismiroglou and 4 other authors
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Abstract: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: this https URL
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Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.26295 [cs.CV]
(or arXiv:2606.26295v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.26295
arXiv-issued DOI via DataCite (pending registration)
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From: Vasiliki Ismiroglou [view email] [v1] Wed, 24 Jun 2026 18:40:45 UTC (12,425 KB)
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