Lingwei Quan

Papers

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About

Lingwei Quan is a leading researcher in underwater computer vision, specializing in enhancing and restoring visual data for autonomous underwater robots. Her work addresses critical challenges in oceanic exploration, where turbidity, light absorption, and scattering degrade image quality. Quan’s most cited paper, "Underwater Image Enhancement based on Deep Learning and Image Formation Model" (2021, 51 citations), pioneered a hybrid approach that combines deep neural networks with physical image formation models, significantly improving clarity and color accuracy in real-world underwater scenes. This breakthrough directly supports robotic tasks in geological surveying, resource extraction, and ecological monitoring. She further advanced the field with "Progressive Attentional Learning for Underwater Image Super-Resolution" (2020, 9 citations), introducing a novel attention mechanism that progressively refines high-resolution outputs from degraded inputs. Quan’s contributions have been recognized for bridging the gap between theoretical models and practical deployment, enabling more reliable visual perception in challenging marine environments. Her work continues to influence the development of robust, real-time imaging systems for autonomous underwater vehicles, with potential applications in deep-sea archaeology and climate change research.