Open Access

ARTICLE

A New Method of Image Restoration Technology Based on WGAN

Wei Fang^1,2,*, Enming Gu¹, Weinan Yi¹, Weiqing Wang¹, Victor S. Sheng³

1 School of Computer & Software, Engineering Research Center of Digital Forensics, Mininstry of Education, Nanjing University of Information Science & Technology, Nanjing, 210044, China
2 Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, 215325, China
3 Texas Tech University, USA

* Corresponding Author: Wei Fang. Email:

Computer Systems Science and Engineering 2022, 41(2), 689-698. https://doi.org/10.32604/csse.2022.020176

Received 12 May 2021; Accepted 16 July 2021; Issue published 25 October 2021

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.

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Keywords

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