He Yinchao, Kang Shuang, Li Wenwen, Xu Hongyan, Liu Sen
School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132022, China.
School of Mechanical and Control Engineering, BaiCheng Normal University, BaiCheng, 137000, China.
Sci Rep. 2024 Jul 6;14(1):15604. doi: 10.1038/s41598-024-66423-0.
Enhancing infrared images is essential for detecting wind turbine blades using infrared technology. This paper introduces an Infrared Image Enhancement Method based on Adaptive Iterative Cutoff Threshold Difference Multi-Scale Top-Hat Transformation (AICT-DMTH) to address the challenge of low image clarity in infrared detection. The method involves performing a black-white difference top-hat transformation by utilizing structural elements of varying scales for dilation and erosion. Additionally, an iterative threshold method is applied to extract more detailed image features, followed by setting a cutoff constant to determine the final scale of the structural element. The effectiveness of the proposed method is evaluated both qualitatively and quantitatively, with infrared images from laboratory and wind farm settings enhanced and compared against existing methods. The experimental results indicate that the proposed method significantly improves the clarity of infrared images, demonstrating robustness in enhancing images from various environments.
增强红外图像对于使用红外技术检测风力涡轮机叶片至关重要。本文介绍了一种基于自适应迭代截止阈值差多尺度顶帽变换（AICT-DMTH）的红外图像增强方法，以应对红外检测中图像清晰度低的挑战。该方法包括利用不同尺度的结构元素进行膨胀和腐蚀来执行黑白差异顶帽变换。此外，应用迭代阈值方法提取更详细的图像特征，然后设置截止常数来确定结构元素的最终尺度。通过对实验室和风力发电场环境的红外图像进行增强并与现有方法进行比较，对所提方法的有效性进行了定性和定量评估。实验结果表明，所提方法显著提高了红外图像的清晰度，在增强各种环境下的图像方面表现出鲁棒性。
