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GGADN: Guided generative adversarial dehazing network

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Abstract

Image dehazing has always been a challenging topic in image processing. The development of deep learning methods, especially the generative adversarial networks (GAN), provides a new way for image dehazing. In recent years, many deep learning methods based on GAN have been applied to image dehazing. However, GAN has two problems in image dehazing. Firstly, For haze image, haze not only reduces the quality of the image but also blurs the details of the image. For GAN network, it is difficult for the generator to restore the details of the whole image while removing the haze. Secondly, GAN model is defined as a minimax problem, which weakens the loss function. It is difficult to distinguish whether GAN is making progress in the training process. Therefore, we propose a guided generative adversarial dehazing network (GGADN). Different from other generation adversarial networks, GGADN adds a guided module on the generator. The guided module verifies the network of each layer of the generator. At the same time, the details of the map generated by each layer are strengthened. Network training is based on the pre-trained VGG feature model and L1-regularized gradient prior which is developed by new loss function parameters. From the dehazing results of synthetic images and real images, the proposed method is better than the state-of-the-art dehazing methods.

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Acknowledgements

This work is supported by East Lake Scholar Program of Wuhan Sports University (Grant No. 201806).

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Authors and Affiliations

  1. College of Sport Engineering and Information Technology, Wuhan Sports University, Wuhan, Hubei, China

    Jian Zhang

  2. College of Economics and Management, Wuhan Sports University, Wuhan, Hubei, China

    Qinqin Dong

  3. College of Computer, Hubei University of Education, Wuhan, Hubei, China

    Wanjuan Song

Authors
  1. Jian Zhang
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  2. Qinqin Dong
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  3. Wanjuan Song
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jiang Zhang, Qinqin Dong and Wanjuan Song. The first draft of the manuscript was written by Jian Zhang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Qinqin Dong.

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Informed consent was obtained from all individual participants included in the study.

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Communicated by Mu-Yen Chen.

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Zhang, J., Dong, Q. & Song, W. GGADN: Guided generative adversarial dehazing network. Soft Comput 27, 1731–1741 (2023). https://doi.org/10.1007/s00500-021-06049-w

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