UAV-Taken Wind Turbine Image Dehazing With a Double-Patch Lightweight Neural Network

Unmanned aerial vehicles (UAVs) offer a solution for remote inspection of wind turbines (WTs). However, in stormy weather conditions, the visual quality of UAV-taken images is affected by contaminated suspended atmospheric particles. To address this problem, a double-patch lightweight convolutional dehazing neural network (DPLDN) is proposed to reconstruct hazy images and enhance the image quality. Unlike other learning-based methods that measure transmission map and atmospheric light separately, the proposed DPLDN uses a transformed atmospheric scattering model to jointly transmission map and atmospheric light, employs depth-separable convolution instead of conventional convolution, and splits the image into double patches. In addition, a super-resolution reconstruction method is proposed to transform the processed low-resolution images into higher-quality images. Extensive experiments show that our proposed method has better dehazing performance compared to other state-of-the-art image dehazing techniques. Meanwhile, the applicability of the method in WT blade image segmentation is experimentally verified.