Considering the disadvantage of traditional dehazing algorithm

a single image dehazing algorithm based on haze layers learning is proposed. According to the atmospheric scattering model

the end-to-end network is designed which directly learn the mapping between the haze images and their corresponding haze layers. The network takes the haze image as the input. Then the recovered haze-free image can be gotten by removing the residual image from the hazy image. Residual learning allows the network to estimate the initial haze layer with relatively high learning rates

which can reduce computational complexity and speed up the convergence process. Otherwise

we use guided filter to refine images avoiding halos and block artifacts

which make the recovered image more similar to the real scene. Finally

the experimental results are analyzed and contrasted carefully. In this paper

the effect on fog images with different fog density is tested

and many comparisons are listed with other classical algorithms. Experiments demonstrate that the proposed algorithm has better results than state-of-the-art methods on both synthetic and real-world images qualitatively and quantitatively.