Jingyi Yuan

Nanjing University of Science and Technology

Papers

Total Citations

H-Index

About

Jingyi Yuan is a researcher at the forefront of space situational awareness and on-orbit servicing, with a specialized focus on enhancing space-based visible imagery in low-light conditions. Their primary research contributions lie at the intersection of computer vision and aerospace engineering, particularly in developing deep learning solutions for the unique challenges of the space environment. Yuan’s most notable work introduces a groundbreaking ground-based dataset and a diffusion model specifically designed for on-orbit low-light image enhancement. This innovation addresses a critical bottleneck: while space-based cameras are economical and lightweight for situational awareness, they are severely hampered by poor illumination. By leveraging advanced deep learning techniques, Yuan’s model significantly improves image quality, enabling more reliable object detection and tracking during crucial on-orbit servicing missions. Although their most-cited papers currently have 3 and 2 citations respectively, reflecting the nascent stage of this specialized field, the work is foundational for future space sustainability. Yuan’s research is essential for students and engineers aiming to advance autonomous operations in space, demonstrating how terrestrial AI techniques can be adapted for the harsh, low-light conditions of orbit.