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GAN-based dehazing network with knowledge transferring

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Abstract

Capturing images under the condition of haze often shows low contrast and fades the color. Restoring the haze-free image from a single image is a challenging task due to the ill-pose of the problem and high degradation. To solve this problem, we propose a GAN (Generative Adversarial Network) Prior Guided Dehazing Network (GPGDN). While the prior dehazing methods often trained the model with adversarial loss to obtain a photorealistic dehazed result, The proposed method explores to transfer of the rich and diverse priors learned from large clean images to dehazing problem. The GPGDN consists of an Encoder and a GAN-based decoder. The Encoder is designed to generate the latent code and noise input, which are fed to GAN-based decoder and generate the final dehazed result. Due to the high degradation of dense haze areas, it is hard to restore high-quality results for these areas. The proposed method can transfer knowledge from the haze-free images into dehazed results and restore high-quality results. The experiment on simulated outdoor hazy images demonstrates that the proposed method outperforms other methods with a significant gap of 3.40dB. Hazy images dehazing by GPGDN show a clear improvement compared to prior methods.

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Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Funding

Shengdong Zhang is supported by the National Natural Science Foundation of China (Nos. U2033210, 82261138629, and 62271321). Shengdong Zhang is partially supported by the Science Project of Shaoxing University (Nos. 2022LG006, 20205048, and 20210026), the Science and Technology Plan Project in Basic Public Welfare class of Shaoxing city (No.2022A1).

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Author notes

  1. Shengdong Zhang and Xiaoqin Zhang contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory of Intelligent Informatics for Safety and Emergency of Zhejiang Province, Wenzhou University, Ouhai, Wenzhou, 325035, Zhejiang, China

    Shengdong Zhang & Xiaoqin Zhang

  2. Computer Science Engineering Department, Shaoxing University, Yuecheng, Shaoxing, 312000, Zhejiang, China

    Shengdong Zhang & En Fan

  3. College of Computer Science and Software Engineering, Shenzhen University, Nanshan, Shenzhen, 518060, Guangdong, China

    Linlin Shen

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  1. Shengdong Zhang
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Correspondence to Shengdong Zhang.

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Zhang, S., Zhang, X., Shen, L. et al. GAN-based dehazing network with knowledge transferring. Multimed Tools Appl 83, 45095–45110 (2024). https://doi.org/10.1007/s11042-023-17226-4

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