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A generative adversarial network for image denoising

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Abstract

Recent studies have shown that the performance of image denoising methods can be improved significantly by using deep convolutional neural networks(CNN). The traditional CNN ways mainly focus on minimizing the Mean Squared Error (MSE), resulting in a feeling that the images lack of high-frequency details. So we apply a generative adversarial network (GAN) in image denoising. A very deep convolutional densenet framework is acting as our generator, which benefits in easing the vanishing-gradient problem of very deep networks. Moreover, we use Wasserstein-GAN as our loss function to stabilize the training process. Also, the Wasserstein distance between real and generated images from discriminator can be regarded as an indicator that has been proved highly relevant to the quality of the generated sample. A photo-realistic image with higher quality can be produced through our work than in traditional ways.

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Acknowledgements

This work was supported by the External Cooperation Program of BIC, Chinese Academy of Sciences, Grant No. 184131KYS820150003.

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Authors and Affiliations

  1. Shanghai University, Shanghai, 200444, People’s Republic of China

    Yue Zhong

  2. Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, 201210, People’s Republic of China

    Yue Zhong, Lizhuang Liu, Dan Zhao & Hongyang Li

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  1. Yue Zhong
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  2. Lizhuang Liu
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  3. Dan Zhao
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  4. Hongyang Li
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Correspondence to Lizhuang Liu.

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Zhong, Y., Liu, L., Zhao, D. et al. A generative adversarial network for image denoising. Multimed Tools Appl 79, 16517–16529 (2020). https://doi.org/10.1007/s11042-019-7556-x

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