A multi-scale attentive recurrent network for image dehazing

Image dehazing is a pre-processing step in computer vision tasks, that has attracted considerable attention from the research community. Existing CNN-based methods ignore haze-related priors and rarely use a coarse-to-fine scheme in a feed-forward architecture to remove haze due to increasing network depth and parameters. This results in sub-optimal dehazing results. To address these problems, a multi-scale attentive recurrent network is proposed for image dehazing, which consists of a haze attention map predicted network and a recurrent encoder-decoder network. First, by assuming that haze in an image is formed by multiple layers with different depths, the haze attention map predicted network is designed for generating the map with multiple stages via a multi-scale recurrent framework. Second, the haze attention map is viewed as the haze-related prior and guides the subsequent recurrent encoder-decoder network to be aware of haze concentration information. Finally, for leveraging the intermediate information and optimizing the dehazing result with less parameters and more robust features, the recurrent residual operations which pass the features of selected layers at the current time step to the corresponding layers at the next time step are applied in the recurrent encoder-decoder network for removing haze following a coarse-to-fine strategy. Experiments on synthetic and real images demonstrate that our method outperforms state-of-the-art methods considering both visual and quantitative evaluations. In addition, our method is also suitable for real-time processing.