| Popis: |
Insufficient light, uneven light, backlighting, and other problems lead to poor visibility of the image of an electric power operation site. Most of the current methods directly enhance the low-light image while ignoring local strong light that may appear in the electric power operation site, resulting in overexposure and a poor enhancement effect. Aiming at the above problems, we propose a low-light image enhancement method for electric power operation sites by considering strong light suppression. Firstly, a sliding-window-based strong light judgment method was designed, which used a sliding window to segment the image, and a brightness judgment was performed based on the average value of the deviation and the average deviation of the subimages of the grayscale image from the strong light threshold. Then, a light effect decomposition method based on a layer decomposition network was used to decompose the light effect of RGB images with the presence of strong light and eliminate the light effect layer. Finally, a Zero-DCE (Zero-Reference Deep Curve Estimation) low-light enhancement network based on a kernel selection module was constructed to enhance the low-light images with reduced or no strong light interference. Comparison experiments using the electric power operation private dataset and the SICE (Single Image Contrast Enhancement) Part 2 public dataset showed that our proposed method outperformed the current state-of-the-art low-light enhancement methods in terms of both subjective visual effects and objective evaluation metrics, which effectively improves the image quality of electric power operation sites in low-light environments and provides excellent image bases for other computer vision tasks, such as the estimation of operators’ posture. |
