Wenqi Ren

Chinese Academy of Sciences, Institute of Information Engineering

Papers

Total Citations

H-Index

About

Wenqi Ren is a prominent researcher specializing in underwater image enhancement and computer vision, with a particular focus on developing advanced algorithms for marine and aquatic applications. His work sits at the intersection of deep learning, image processing, and robotics, addressing critical challenges in underwater visual perception where degraded image quality poses significant obstacles to marine engineering and aquatic robotics systems. Ren's most influential contribution is his development of a comprehensive underwater image enhancement benchmark dataset, published in 2019 and accumulating 57 citations, which addressed a fundamental gap in the field — the lack of standardized evaluation frameworks for comparing enhancement algorithms. By providing a rigorous benchmark, he enabled more systematic and reproducible research across the community. Building on this foundation, his 2021 work introduced a sophisticated multi-scale deformable convolution network incorporating attention mechanisms for autonomous underwater robots, garnering 34 citations and demonstrating practical real-world deployment of his enhancement techniques. Together, these contributions reflect Ren's commitment to advancing both the theoretical foundations and practical applications of underwater imaging. His research has meaningfully shaped how scholars evaluate and develop next-generation underwater vision systems, making him an important figure for students exploring marine robotics and computational imaging.