Yibei Wu

Papers

Total Citations

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About

Yibei Wu is a researcher at the forefront of underwater robotics and computer vision, with a specialized focus on deep learning-driven image enhancement. Their most cited work, a comprehensive 2024 review on underwater image enhancement based on deep learning, has already garnered 3 citations, underscoring its timely relevance in a rapidly evolving field. Wu’s major contribution lies in systematically analyzing how deep learning techniques—from convolutional neural networks to generative adversarial networks—can overcome the unique challenges of underwater imaging, such as color distortion, low contrast, and light attenuation. By synthesizing recent advancements, this review serves as a critical roadmap for researchers and engineers working to improve autonomous underwater vehicle perception and marine exploration. Wu’s work bridges the gap between theoretical deep learning models and practical underwater applications, offering clear benchmarks and future directions. Their research not only advances the robustness of underwater vision systems but also supports broader efforts in environmental monitoring, underwater archaeology, and offshore infrastructure inspection. As deep learning continues to transform underwater robotics, Wu’s contributions provide essential guidance for both newcomers and seasoned experts in the field.