An End-to-End System for Unmanned Aerial Vehicle High-Resolution Remote Sensing Image Haze Removal Algorithm Using Convolution Neural Network

Abstract

An end-to-end image dehazing method based on convolution neural network is presented to solve the problem in which Unmanned Aerial Vehicle (UAV) high-resolution remote sensing images have reduced image sharpness due to haze. First, the original atmospheric scattering model is adapted to get an end-to-end dehazing model. Then, several unknown parameters are unified into one parameter, and the unknown parameter is estimated by using a multiscale convolution neural network. Finally, the parameter estimates are incorporated into the dehazing model to get a haze-free image. For the no reference image dataset, we first train the network using existing datasets, and then the network is trained using a self-built dataset. In this article, the haze removal effect for different types of unmanned remote sensing images is tested and compared with those of the main dehazing algorithms. The experiments show that the algorithm in this article has different degrees of improvement regarding its visual effect and objective indicators.