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DRCDN: learning deep residual convolutional dehazing networks

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Abstract

Single image dehazing, which is the process of removing haze from a single input image, is an important task in computer vision. This task is extremely challenging because it is massively ill-posed. In this paper, we propose a novel end-to-end deep residual convolutional dehazing network (DRCDN) based on convolutional neural networks for single image dehazing, which consists of two subnetworks: one network is used for recovering a coarse clear image, and the other network is used to refine the result. The DRCDN firstly predicts the coarse clear image via a context aggregation subnetwork, which can capture global structure information. Subsequently, it adopts a novel hierarchical convolutional neural network to further refine the details of the clean image by integrating the local context information. The DRCDN is directly trained using complete images and the corresponding ground-truth haze-free images. Experimental results on synthetic datasets and natural hazy images demonstrate that the proposed method performs favorably against the state-of-the-art methods.

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Acknowledgements

We thank anonymous reviewers very much for their suggestive comments. This work is partially supported by the NSFC (No. 61472289, 41571436).

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  1. School of Computer Science, Wuhan University, Wuhan, China

    Shengdong Zhang & Fazhi He

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  1. Shengdong Zhang
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Correspondence to Fazhi He.

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Zhang, S., He, F. DRCDN: learning deep residual convolutional dehazing networks. Vis Comput 36, 1797–1808 (2020). https://doi.org/10.1007/s00371-019-01774-8

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