Single-image Dehazing Algorithm Based on Convolutional Neural Networks

Abstract

The paper proposes a new single image dehazing method based on a convolutional neural network. Our method directly learns an end to end mapping between the haze images and their corresponding haze layers (i.e. residual images between haze images and non-haze images). A convolutional neural network takes the haze image as an input and the residual image as an output. Then, a recovered dehazed image can be obtained by removing the residual from the haze image. Residual learning allows the network to directly estimate the initial haze layer with relatively high learning rates, which reduce computational complexity and speed-up the convergence process. Since the initial haze layer is only approximate, we use a guided filter to refine this layer to avoid halos and block artefacts, which makes the recovered image more similar to a real scene. The algorithms are tested on fog images with different fog densities. Comparisons are provided with other dehazing algorithms. Experiments demonstrate that the proposed algorithm outperforms state-of-the-art methods on both synthetic and real-world images, qualitatively and quantitatively.