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Journal of Graphics ›› 2023, Vol. 44 ›› Issue (5): 928-936.DOI: 10.11996/JG.j.2095-302X.2023050928

• Image Processing and Computer Vision • Previous Articles     Next Articles

Image defogging algorithm based on YUV color space GAN network

XU Zhen-dong(), ZHANG Tian-yu, ZHANG Shi-heng, YAO Cong-rong, WANG Dao-lei()   

  1. College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, China
  • Received:2023-04-23 Accepted:2023-06-18 Online:2023-10-31 Published:2023-10-31
  • Contact: WANG Dao-lei (1981-), professor, Ph.D. His main research interests cover machine learning, machine vision and image processing, etc. E-mail:alfredwdl@shiep.edu.cn
  • About author:XU Zhen-dong (1999-), master student. His main research interests coverdigital image processing and image reconstruction in 3D. E-mail:935510065@qq.com
  • Supported by:
    National Natural Science Foundation of China(61502297)


To address the current problems of chromatic aberration and unsatisfactory defogging effects in the single-image defogging algorithm, we proposed a single-image defogging algorithm based on YUV color space. This method applied the idea of GAN image coloring task to recolor haze images from a positive perspective. The haze image was converted to the YUV color space, and the dense residual module was employed to collect the brightness features of the image from the Y channel. Additionally, the brightness information of the haze image was adjusted according to the characteristics, mitigating the impact of haze on the image. Four residual modules were used on the UV channel to extract image color information multiple times, and recolored the image through model prediction based on the extracted color information. A feature fusion network, including a skip connection structure, was utilized to fuse low-level features with high-level ones. Furthermore, the addition of an attention module during the fusion process led to more refined dehazing. The experimental results demonstrated the algorithm’s efficacy, showcasing remarkable performance in terms of RMSE, SSIM, and PSNR on the synthetic haze image datasets. On the real haze image, the algorithm displayed excellent performance on dense fog and thin fog images, ultimately leading to an outstanding defogging effect and ensuring a high level of stability.

Key words: GAN, image dehazing, recolor, YUV color space, jump connection

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