Dehuan Zhang

Papers

Total Citations

H-Index

About

Dehuan Zhang is a leading researcher in underwater robotics and computer vision, specializing in perceptual systems that enable intelligent machines to operate in challenging aquatic environments. His work focuses on overcoming severe image degradation—including color distortion, texture blurring, and detail loss—that plagues underwater visual data. Zhang’s major contributions include pioneering degradation-decoupling enhancement frameworks for robot vision, which significantly improve the clarity and reliability of visual inputs for autonomous monitoring and manipulation tasks. His most-cited paper, "Degradation-Decoupling Vision Enhancement for Intelligent Underwater Robot Vision Perception System" (2025, 7 citations), addresses critical gaps in existing enhancement methods by effectively separating and correcting multiple degradation factors. He has also advanced the field with semantic-guided diffusion models for water-related image enhancement (4 citations) and depth-aware reconstruction techniques for large-view underwater scenes (2 citations). Zhang’s innovative approaches are laying the groundwork for more robust, real-world deployment of autonomous underwater vehicles, with direct implications for marine science, infrastructure inspection, and environmental monitoring.