Single Image Dehazing Based on Learning of Haze Layers

This paper proposes a new haze layer based single image dehazing algorithm. The residual images, which exists between the hazy images and the clear images, will be firstly obtained by haze layers through an end-to-end mapping from the original hazy images. Then the designed convolutional neural network can remove the residual image from the given hazy image to obtain a recovered dehazed image. In order to remove the halos and block artefacts in the dehazed images, a guided filter is applied before generating the final dehazed images, which will also increase the reality of the result images. Since the proposed haze layer based dehazing algorithm directly learns the residual images, it enjoys a relatively high learning rate and low computation. We tested and compared the proposed algorithm with other dehazing methods in various variables, such as the density of the fog and the testing scenes, e.g. real-world images or synthetic images. Experimental results showed the advancement of the proposed method in many metrics.