Research On De-Fogging Algorithm Of UAV Aerial Images Based On 3D Reconstruction Application Scenarios

With the gradual popularity of UAVs,aerial photography by UAVs has become an effective way of image acquisition owing to its benefit such as real-time,suitability and adaptability.But due to the hazy weather,the image quality captured by the drone becomes low,which seriously affects the use of subsequent aerial drone images.So,this paper analyzes the defogging algorithms at home and abroad and the UAV and the common fog image,then discusses the difference between them,proposes two defogging methods for UAV aerial images,and finally,in order to reflect the effectiveness of the two algorithms,applies the defogged images to 3D reconstructed scenes.This paper focuses on the following.(1)Based on the atmospheric scattering model,this article analyses the reasons for the poor image quality caused by fog.The paper provides an in-depth analysis of traditional and deep learning based defogging methods and compares their effectiveness in defogging.The drawbacks and shortcomings of the algorithms are further investigated.(2)In this paper,based on the analysis of the differences between conventional images and UAV aerial images,a comprehensive image quality evaluation function is proposed for the de-fogging of UAV aerial images by combining the characteristics of UAV aerial images.The algorithm estimates the atmospheric light intensity using the quadtree cycle hierarchical search method,and evaluates the image transmittance value using the image comprehensive quality evaluation function established by the median Fourier amplitude,information entropy and average gradient,and finally obtains the defogged image using the principle of atmospheric scattering model.The experimental results show the superiority of this method over synthetic fog images obtained from UAV aerial photographs and aerial images in the natural environment.(3)To overcome the shortage of feature extraction and inaccurate parameter estimation in AOD-Net,a new method is proposed.The method first normalizes the third and fourth convolutional layers of the AOD-Net to make its convergence rate faster.Secondly,a submodule of attention to features(AFF)is introduced to get more context information,finally,combine the three layers of the network model with the null convolutions to improve the defogging images.The aerial images of the de-fogged UAV are obtained due to the variant atmospheric scattering model.The effectiveness of the algorithm was demonstrated through experiments on the algorithm.(4)The image acquisition was performed using UAV,the synthetic fog processing was performed on the acquired dataset,and the synthetic UAV aerial fog map was used as the dataset for the defogging algorithm,and then the images before and after the defogging process were reconstructed in 3D to compare the reconstruction results before and after defogging,and the reconstruction results showed that the reconstruction effect of the defogged UAV aerial images was significantly better than that before defogging,which verified the practical value of the two proposed algorithms and also provided new ideas for the application of UAV aerial images to a wider range of fields such as disaster prevention or rescue.