The application of unmanned aerial system (UAV) remote sensing technology as a unique means of spatial data collection in various fields is based on high-resolution images captured by UAVs. However, due to the influence of atmospheric water vapor, haze, and other factors, the quality of UAV aerial images is limited. Considering that the model-based image fogging method developed according to the dark channel theory is widely used, the image edge after fogging incurs a “halo” effect owing to the excessive coarseness of the transmission map. We take advantage of the nonlocal structure tensor of the image to better protect the important details of the image edge while removing noise. The images we used are obtained from the public multi real world fog image dataset. Experiment results demonstrate that the proposed method can effectively eliminate the halo effect that occurs at the edges of an image after demisting.