Beyond Aesthetics: Quantifying Information Loss in Turbid Scenes

2026-06-24

AI Summary

This work addresses the severe degradation of visual information in turbid underwater environments and the inadequacy of existing synthetic datasets in realistically modeling such degradation, which leads to distorted model evaluation. To bridge this gap, the authors introduce TUB, the first large-scale benchmark dataset of real-world extremely turbid underwater images, comprising 1,320 images annotated with over 16,000 high-confidence instance masks. Furthermore, they propose Phase Congruency-based Degradation (PCD), a novel metric for quantifying turbidity-induced degradation that overcomes the sensitivity of conventional methods to contrast variations. PCD achieves, for the first time, strong correlation with instance segmentation performance and significantly outperforms existing evaluation metrics on both real and synthetic turbid images, thereby establishing a reliable benchmark for underwater vision tasks.

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: https://vap.aau.dk/pcd

Problem

• turbid underwater
• information loss
• computer vision
• synthetic datasets
• visibility degradation

Innovation

• turbid underwater imaging
• information loss quantification
• phase congruency
• instance segmentation
• real-world dataset