A new method of image restoration technology based on WGAN

Wei Fang, Enming Gu, Weinan Yi, Weiqing Wang, Victor S. Sheng

Research output: Contribution to journalArticlepeer-review

Abstract

With the development of image restoration technology based on deep learning, more complex problems are being solved, especially in image semantic inpainting based on context. Nowadays, image semantic inpainting techniques are becoming more mature. However, due to the limitations of memory, the instability of training, and the lack of sample diversity, the results of image restoration are still encountering difficult problems, such as repairing the content of glitches which cannot be well integrated with the original image. Therefore, we propose an image inpainting network based on Wasserstein generative adversarial network (WGAN) distance. With the corresponding technology having been adjusted and improved, we attempted to use the Adam algorithm to replace the traditional stochastic gradient descent, and another algorithm to optimize the training used in recent years. We evaluated our algorithm on the ImageNet dataset. We obtained high-quality restoration results, indicating that our algorithm improves the clarity and consistency of the image.

Original languageEnglish
Pages (from-to)689-698
Number of pages10
JournalComputer Systems Science and Engineering
Volume41
Issue number2
DOIs
StatePublished - 2022

Keywords

  • Context semantic
  • DCGAN
  • Image restoration
  • WGAN

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