Region-Adaptive Transmittance Adjustment Algorithm for Dehazing Transmission Line Surveillance Images

With the advancement of smart grid construction, power systems have imposed higher requirements for the safety surveillance of transmission lines. However, due to the complex and variable environment, the atmospheric scattering effect significantly reduces the visibility of visual surveillance systems and affects the accuracy of transmission line status analysis. To address these challenges, this paper proposes an improved dark channel prior dehazing algorithm based on regionally adaptive adjustments of the transmittance factor. By implementing grayscale-based region segmentation, images are divided into light and dark regions, allowing for specific fog condition assessments in the dark regions. High-brightness images and overexposed regions are processed separately. This method effectively resolves issues of inaccurate fog condition assessment and color distortion when dealing with sky-dominant scenes. Experimental results demonstrate that the proposed method is more suitable for processing transmission line surveillance images, significantly enhancing the dehazing effect and providing reliable technical support for the safety surveillance of power systems.