Wei Fang1,2,*, Enming Gu1, Weinan Yi1, Weiqing Wang1, Victor S. Sheng3
Computer Systems Science and Engineering, Vol.41, No.2, pp. 689-698, 2022, DOI:10.32604/csse.2022.020176
- 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 More >
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