Journal of Northeastern University(Natural Science) ›› 2022, Vol. 43 ›› Issue (11): 1591-1598.DOI: 10.12068/j.issn.1005-3026.2022.11.010

• Mechanical Engineering • Previous Articles     Next Articles

Generative Adversarial Network Based on Multi-scale Dense Feature Fusion for Image Dehazing

LIAN Jing1,2, CHEN Shi1, DING Kun3, LI Lin-hui1,2   

  1. 1. School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China; 2. State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, China; 3. Applied Technology College, Dalian Ocean University, Dalian 116000, China.
  • Published:2022-12-06
  • Contact: LI Lin-hui
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Abstract: In view of the poor dehazed effect of the existing dehazing networks in real hazy image and the obvious noise in the sky area of the image, a generative adversarial network based on multi-scale dense feature fusion for image dehazing is proposed. The dehazing network uses the produced synthetic foggy data set for adversarial training. Firstly, the dehazing network is designed and the network model is constructed; secondly, a realistic foggy data set is directly generated from the synthetic sunny weather image using deep tags to be suitable for the dehazed field; finally, the network is tested on the real foggy day data set and selects six representative deep learning dehazing networks in recent years for comparison, and non-reference image quality evaluation indicators are used for objective analysis. The research results show that the effect of the proposed dehazing network in real scenes is significantly improved compared to the other networks. The subjective visual effect is significantly better, and the comprehensive performance is better than the other networks in non-reference image quality evaluation indicators.

Key words: image processing; image dehazing; generative adversarial network; multi-scale dense feature fusion; adversarial training

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