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DRCDN: learning deep residual convolutional dehazing networks

  • Shengdong Zhang
  • Fazhi HeEmail author
Original Article
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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.

Keywords

Residual learning Dehazing Image restoration Global structure information Deep learning 

Notes

Acknowledgements

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

Compliance with ethical standards

Conflict of interest

The authors declares that there is no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer ScienceWuhan UniversityWuhanChina

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