Dynamic Illumination and Visual Enhancement of Surface Inspection Images of Turbid Underwater Concrete Structures

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Aiming at the problem of image quality degradation caused by turbid water, non-uniform illumination, and scattering effect in the surface defect detection of underwater concrete structures, firstly, the concrete surface images under different shooting distances, different sediment concentrations, and different illumination conditions were collected through laboratory experiments to simulate the concrete surface images of a reservoir dam with higher sediment concentration and deeper water depth. On this basis, an underwater image enhancement algorithm named DIVE (Dynamic Illumination and Vision Enhancement) is proposed. DIVE solves the problems of luminance unevenness and color deviation in stages through the illumination–scattering decoupling processing framework, and combines efficient computing optimization to achieve real-time processing. The lighting correction of Gaussian distributions (dynamic illumination module) was processed in stages with suspended particle scattering correction (visual enhancement module), and the bright and dark areas were balanced and color offset was corrected by local gamma correction in Lab space and dynamic decision-making of G/B channel. Through thread pool parallelization, vectorization and other technologies, the real-time requirement can be achieved at the resolution of 1920 × 1080. Tests show that DIVE significantly improves image quality in water bodies with sediment concentration up to 500 g/m3, and is suitable for complex scenes such as reservoirs, oceans, and sediment tanks.
Funding Information
  • National Natural Science Foundation of China (52279140, 52479133, U2443230, 2023-JC-YB-358, 22JY044, 2022TD-01)
  • Yellow River Water Science Research Joint Fund (52279140, 52479133, U2443230, 2023-JC-YB-358, 22JY044, 2022TD-01)
  • Natural Science Basic Research Project of Shaanxi Province (52279140, 52479133, U2443230, 2023-JC-YB-358, 22JY044, 2022TD-01)
  • Key Scientific Research Project of Shaanxi Provincial Department of Education (52279140, 52479133, U2443230, 2023-JC-YB-358, 22JY044, 2022TD-01)
  • Shaanxi Provincial Innovative Research Team (52279140, 52479133, U2443230, 2023-JC-YB-358, 22JY044, 2022TD-01)
  • Jie Yang (National Natural Science Foundation of China (Grant Nos. 52279140）)
  • Lin Cheng (National Natural Science Foundation of China (Grant Nos. 52479133), National Natural Science Foundation of China Yellow River Water Science Research Joint Fund Project (Grant Nos. U2443230), Natural Science Basic Research Project of Shaanxi Province(Grant Nos. 2023-JC-YB-358))

This publication has 31 references indexed in Scilit: