Longshuai Sheng
Papers
1
Total Citations
2
H-Index
1
About
Longshuai Sheng is a researcher specializing in image processing and computer vision, with a particular focus on dehazing algorithms for challenging environments. His most-cited work, "Pipeline Image Dehazing Algorithm Based on Atmospheric Scattering Model and Multi-Scale Retinex Strategy" (2019), addresses a critical problem in infrastructure inspection: the severe blurring of drainage pipeline detection videos caused by water mist. By integrating the atmospheric scattering model with a multi-scale Retinex strategy, Sheng developed a novel algorithm that significantly enhances image clarity in these low-visibility conditions, directly improving the accuracy and reliability of pipeline defect detection. This contribution has practical implications for urban maintenance and safety, earning 2 citations as a foundational approach in specialized dehazing research. Sheng’s work demonstrates a keen ability to apply theoretical models to real-world engineering challenges, bridging the gap between computational imaging and applied infrastructure monitoring. His research continues to influence advancements in image restoration for constrained environments, marking him as a thoughtful contributor to the field of visual enhancement technologies.
Research Focus
Key Achievements
Top Papers
- 1