Jinjin Ye
Papers
1
Total Citations
3
H-Index
1
About
Jinjin Ye is a researcher specializing in computer vision and underwater image processing, with a particular focus on enhancing visual data captured in challenging aquatic environments. Their most-cited work, "An Underwater Image Color Correction Algorithm Based on Underwater Scene Prior and Residual Network" (2022), introduces a novel approach that combines domain-specific scene priors with deep residual learning to correct color distortions common in underwater imagery. This contribution addresses a critical bottleneck in marine robotics, environmental monitoring, and underwater archaeology, where accurate color representation is essential for analysis and decision-making. While the paper has garnered 3 citations to date, its methodological integration of physical priors with neural networks represents a promising direction for robust image restoration under non-ideal conditions. Ye’s research bridges the gap between traditional physics-based models and data-driven techniques, offering practical solutions for real-world underwater vision systems. Their work continues to inspire further exploration into adaptive algorithms that can handle diverse water types and lighting conditions, positioning them as an emerging voice in the field of computational imaging.
Research Focus
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Top Papers
- 1