Compression artifacts reduction by improved generative adversarial networks

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

18 Scopus Citations
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Author(s)

  • Zengshun Zhao
  • Qian Sun
  • Haoran Yang
  • Heng Qiao
  • Zhigang Wang

Detail(s)

Original languageEnglish
Article number62
Journal / PublicationEurasip Journal on Image and Video Processing
Volume2019
Online published16 May 2019
Publication statusPublished - 2019
Externally publishedYes

Link(s)

Abstract

In this paper, we propose an improved generative adversarial network (GAN) for image compression artifacts reduction task (artifacts reduction by GANs, ARGAN). The lossy compression leads to quite complicated compression artifacts, especially blocking artifacts and ringing effects. To handle this problem, we choose generative adversarial networks as an effective solution to reduce diverse compression artifacts. The structure of “U-NET” style is adopted as the generative network in the GAN. A discriminator network is designed in a convolutional manner to differentiate the restored images from the ground truth distribution. This approach can help improve the performance because the adversarial loss aggressively encourages the output image to be close to the distribution of the ground truth. Our method not only learns an end-to-end mapping from input degraded image to corresponding restored image, but also learns a loss function to train this mapping. Benefit from the improved GANs, we can achieve desired results without hand-engineering the loss functions. The experiments show that our method achieves better performance than the state-of-the-art methods.

Research Area(s)

  • CNN, Compression artifacts, GANs, JPEG compression

Citation Format(s)

Compression artifacts reduction by improved generative adversarial networks. / Zhao, Zengshun; Sun, Qian; Yang, Haoran et al.
In: Eurasip Journal on Image and Video Processing, Vol. 2019, 62, 2019.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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