GAN-based dehazing network with knowledge transferring

Shengdong Zhang, Xiaoqin Zhang, Linlin Shen, En Fan

Research output: Journal PublicationArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)45095-45110
Number of pages16
JournalMultimedia Tools and Applications
Issue number15
Publication statusPublished - May 2024
Externally publishedYes


  • Dehazing
  • Dense haze
  • GAN prior
  • Real image dehazing

ASJC Scopus subject areas

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications


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