Mingzhe Sun
Papers
1
Total Citations
2
H-Index
1
About
Mingzhe Sun is a researcher specializing in underwater imaging and autonomous robotic vision, with a particular focus on enhancing image quality in challenging aquatic environments. His key research areas include underwater image enhancement, turbidity-adaptive algorithms, and light attenuation modeling for robotic systems. Sun’s most notable contribution is his 2018 paper, "Underwater Image Enhancement Algorithm Adapted to Different Turbidities Ranges," which introduced a novel method that dynamically adjusts to varying water clarity. By establishing an illumination intensity attenuation model based on a robot’s onboard light source, he integrated absorption and scattering factors to significantly improve image visibility across turbidity levels. This work, cited 2 times, provides a foundational approach for autonomous underwater vehicles operating in unpredictable conditions. Sun’s research bridges the gap between theoretical optics and practical robotics, offering scalable solutions for marine exploration, inspection, and environmental monitoring. His achievements highlight a commitment to advancing real-world applications of computer vision in underwater domains, making his work valuable for students and engineers developing robust perception systems for aquatic robotics.
Research Focus
Key Achievements
Top Papers
- 1