Image defogging algorithm combined with full convolution neural network

Aiming at the problems of contrast reduction, saturation reduction and color migration of images collected in foggy environment, an image defogging algorithm based on full convolution neural network is put forward. First, the proposed three scales convolution neural network is used to study the fog of the mapping relationship between foggy image and medium transmission map, gradually produce the refine medium transmission map; secondly, the foggy image is used as a guide map to refine the forecasting medium transmission map, so as to make the edge information of the image more smooth; finally, the value of atmospheric light is estimated according to the dark channel prior theory, and the fog-free image is recovered by the atmospheric scattering model. The fog-free image obtained by this method not only causes no loss of useful information in the image, but also restores the color of the image naturally. Experimental results show that the algorithm proposed is superior to other comparison algorithms in both natural fog images and fog images produced by Middlebury Stereo Datasets, and the restored images have better contrast and clarity.