Improved multi-scale convolutional neural network about image defogging method

In recent years, the frequency of fog, haze and other bad weather phenomena has increased, and the suspended particles in the fog directly affect people's work in acquiring clear images from outdoors. The degraded image acquired on foggy days, the quality of which is shown as blurred, seriously affecting people's work on the identification and extraction of image feature information. Clear defogging of foggy images has an important research value in the fields of traffic monitoring, military and civilian aerial photography, target tracking, remote sensing satellites and so on. For many existing image defogging methods generally have long time-consuming, low efficiency and prone to distortion and other problems, this paper is based on the mechanism of deep learning technology, the use of conventional convolutional neural network model defogging method to carry out in-depth research and discussion, proposed a multi-scale convolutional neural network based on the improvement of image defogging algorithm.