Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (5): 168-172.DOI: 10.3778/j.issn.1002-8331.2003-0336

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Improved Image Denoising Generative Adversarial Network Algorithm

CHEN Renhe, LAI Zhenyi, QIAN Yurong   

  1. School of Software, Xinjiang University, Urumqi 830046, China
  • Online:2021-03-01 Published:2021-03-02



  1. 新疆大学 软件学院,乌鲁木齐 830046


The existence of image noise will disturb people’s understanding of the image. In order to effectively remove the noise and obtain a better visual perception, an algorithm based on generative adversarial network is proposed. This algorithm obtains more image features by increasing the width of the generative network. And it adds a global residual to the feature extraction and learning of the input noise image to avoid feature loss. The network uses a weighted sum of the anti-loss and reconstruction loss, which can effectively retain the details of the image while removing noise. Experimental results show that the algorithm can effectively remove image noise and improve the visual perception of the image.

Key words: generative adversarial network, image denoising, global residual, reconstruction loss



关键词: 生成对抗网络, 图像去噪, 全局残差, 重建损失