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Joint Dual-Branch Denoising for Underwater Stereo Depth Estimation
Jingxin Zhou1, Yeqi Hu1, Yuan Rao1
1College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China.
Sensors (Basel, Switzerland)
|November 27, 2025
Summary
This study introduces Joint Dual-Branch Denoising (JDBD), a novel framework enhancing underwater depth estimation. JDBD improves accuracy and visual quality in challenging marine environments.
Area of Science:
- Computer Vision
- Robotics
- Marine Exploration
Background:
- Underwater imaging faces degradation from light attenuation, scattering, and distortion.
- Limited real stereo data hinders accurate depth estimation in marine environments.
Purpose of the Study:
- To develop a robust framework for accurate underwater depth estimation.
- To address image degradation challenges in underwater scenes.
Main Methods:
- Proposed Joint Dual-Branch Denoising (JDBD) as a plug-in for dual-branch depth estimation networks.
- Implemented task-aware denoising with bidirectional refinement between monocular and stereo pathways.
- Utilized Adaptive White Balance, Red Inverse Channel Prior, and Joint Bilateral Filtering for image correction.
Main Results:
- JDBD achieved high depth estimation accuracy and visual fidelity on real-world and synthetic underwater datasets.
- Demonstrated robust performance across diverse underwater conditions.
- Significantly improved visual quality and depth accuracy compared to existing methods.
Conclusions:
- JDBD framework effectively enhances underwater depth estimation.
- The proposed methods successfully mitigate image degradation challenges.
- JDBD offers a adaptable solution for various underwater applications.
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