Xuelei Chen

Papers

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About

Xuelei Chen is a leading researcher in underwater computer vision, specializing in image enhancement and super-resolution for autonomous underwater systems. Their work addresses critical challenges in oceanic robotics, where turbidity, light absorption, and scattering degrade visual perception. Chen’s most cited paper (2021, 51 citations) pioneered a deep learning framework that integrates image formation models to restore clarity in underwater scenes, significantly improving the reliability of robotic vision for geological exploration and resource management. They further advanced the field with a progressive attentional learning approach (2020) for underwater image super-resolution, enabling finer detail recovery from degraded inputs. By bridging physical optics and neural network architectures, Chen’s contributions have direct applications in marine ecology, deep-sea mining, and environmental monitoring. Their research not only enhances the autonomy of underwater robots but also sets new benchmarks for image quality in extreme aquatic environments, earning recognition as a vital resource for engineers and scientists working on subsea vision systems.