Papers
3
Total Citations
31
H-Index
2
About
Dr. Fengling Jiang is a rising researcher at the intersection of computer vision, autonomous systems, and underwater imaging. Her work is distinguished by a focus on enhancing visual perception in challenging environments—particularly underwater and for self-driving cars—using advanced deep learning architectures. Dr. Jiang’s most impactful contribution is the development of TEGAN (Transformer Embedded Generative Adversarial Network), a pioneering framework for underwater image enhancement that has already garnered 22 citations since its 2023 publication. This work addresses the critical problem of color distortion and low visibility in underwater scenes, with applications in marine robotics and environmental monitoring. She further advances autonomous driving safety through a novel cognitively inspired deep learning approach for detecting drivable areas, demonstrating her ability to bridge human visual cognition with machine learning. Most recently, her 2025 paper on multi-scale integration with semantic embedding and adaptive excitation transformers continues to push the boundaries of underwater optical image enhancement. Dr. Jiang’s research not only contributes foundational techniques to computer vision but also holds practical promise for real-world deployment in autonomous navigation and underwater exploration.
Research Focus
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Top Papers
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