Robust Image Processing Techniques for Construction Environment Monitoring Using Underwater Robots

2026-07-02

AI Summary

This work addresses the degradation of visual quality in underwater images captured in real oceanic environments, which is primarily caused by depth-dependent forward scattering blur and marine snow artifacts. To tackle this challenge, the authors propose a staged image enhancement framework that explicitly models the depth-dependent forward scattering effect for the first time and extracts realistic marine snow degradation patterns from authentic underwater imagery. These components are leveraged to generate high-fidelity synthetic data for fine-tuning a Joint-ID network, followed by a lightweight contrast enhancement post-processing step. The approach effectively bridges the domain gap between synthetic and real underwater images, yielding significant improvements in UIQM scores and perceptual clarity on a real-world dataset collected off the coast of Korea, thereby enhancing the usability of underwater imagery for robotic operations.

Abstract

This paper proposes a robust image processing framework for underwater robot-based construction environment monitoring, targeting complex degradations observed in real marine environments. Unlike conventional approaches that mainly consider absorption and backscattering, real underwater imagery is strongly affected by depth-dependent forward scattering blur and particle-induced degradations such as marine snow. To address this, we introduce a staged processing pipeline that sequentially models background degradation via depth-aware forward scattering and foreground degradation using realistic marine snow patterns extracted from real images. The resulting synthetic data are used to retrain an existing Joint-ID network without modifying its architecture, enabling an isolated evaluation of dataset realism. In addition, a lightweight post-processing scheme is applied to enhance contrast and structural clarity. Experiments on real underwater datasets collected in Korean coastal environments demonstrate consistent improvements in visual quality and UIQM scores. The results indicate that explicitly modeling forward scattering and realistic particle effects effectively reduces the synthetic-to-real gap and improves practical applicability in real-world underwater robotic operations.

Problem

• underwater image degradation
• forward scattering
• marine snow
• construction environment monitoring
• underwater robots

Innovation

• forward scattering
• marine snow
• synthetic-to-real gap
• underwater image enhancement
• depth-aware degradation modeling