Translator Disclaimer
Paper
12 June 2020 Feature guidance GAN for high quality image restoration
Author Affiliations +
Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 115190L (2020) https://doi.org/10.1117/12.2572942
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan
Abstract
Here we propose a novel image inpainting model DFG-GAN, which can effectively alleviate the artifacts problem when the missing region area is too large. Unlike other image inpainting models, our model can transfer the image inpainting task into a GAN task when the mask fills the total image. Apart from that, we also take advantage of the extra class label information to tell what kind of the damaged image is. The more information feed in, the better result shall be. Experiments on several publicly available datasets demonstrate the advantage of the proposed method over existing approaches, regarding both visual fidelity and margin texture.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ziqiang Pei, Sheng Yang, and Guoyou Wang "Feature guidance GAN for high quality image restoration", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 115190L (12 June 2020); https://doi.org/10.1117/12.2572942
PROCEEDINGS
10 PAGES


SHARE
Advertisement
Advertisement
Back to Top