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About

Peigen Luo is a researcher specializing in underwater computer vision, deep learning, and autonomous robotic perception. His work focuses on developing advanced computational methods to address the unique visual challenges posed by underwater environments, including poor image quality, color distortion, and limited resolution — obstacles that significantly hinder the effectiveness of autonomous underwater vehicles. Luo's most impactful contribution is the development of Deep SESR, a residual-in-residual generative network that simultaneously enhances image quality and performs super-resolution for underwater imagery, enabling near real-time visual processing for robotic systems. This pioneering work on the Simultaneous Enhancement and Super-Resolution (SESR) problem has garnered 38 citations, reflecting its strong reception in the robotics and computer vision communities. He has also made foundational contributions to underwater scene understanding through SUIM — the first large-scale semantically annotated underwater image dataset, covering eight object categories including divers, fish, and underwater robots — cited 17 times since its 2020 release. Additionally, his deep residual multiplier-based approach to single image super-resolution further demonstrates his commitment to practical, deployable solutions for autonomous marine systems. Collectively, Luo's research is shaping how underwater robots see and interpret their environments.