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Progressive image dehazing network based on dual feature extraction modules

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Abstract

Image dehazing is of great importance and has been widely studied, as haze severely affects many high-level computer vision tasks. In this paper, by considering the gradual dissipation process of haze, a progressive dehazing network (PDN) is proposed. The proposed approach realizes haze removal step by step by constructing two main modules: the preliminary and fine dehazing modules. In the preliminary dehazing module, a combined residual block is first constructed to extract and enhance features of different levels. Then, an adaptive feature fusion strategy is designed to integrate these features and output the initial dehazing result. Aiming at the residual haze in the initial results, a fine dehazing module is constructed by simulating the last period of the haze dissipation process to further extract a fine haze layer. The final dehazing result is obtained by removing the fine haze layer from the initial dehazing result. Experimental results indicate that the proposed method is superior to some state-of-the-art dehazing methods in terms of visual comparison and objective evaluation.

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Data availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61862030, No. 62072218, and 62261025), by the Natural Science Foundation of Jiangxi Province (No. 20182BCB22006, No. 20181BAB202010, No. 20192ACB20002, and No. 20192ACBL21008), and by the Talent project of Jiangxi Thousand Talents Program (No. jxsq2019201056).

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Authors and Affiliations

  1. School of Computer Science and Technology, Tiangong University, Tianjin, 300387, China

    Yong Yang

  2. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, 330032, China

    Wei Hu & Juwei Guan

  3. School of Software, Tiangong University, Tianjin, 300387, China

    Shuying Huang

  4. School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang, 330032, China

    Weiguo Wan

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  1. Yong Yang
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Correspondence to Shuying Huang.

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Yang, Y., Hu, W., Huang, S. et al. Progressive image dehazing network based on dual feature extraction modules. Int. J. Mach. Learn. & Cyber. 14, 2169–2180 (2023). https://doi.org/10.1007/s13042-022-01753-x

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  • DOI: https://doi.org/10.1007/s13042-022-01753-x

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