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http://dx.doi.org/10.5909/JBE.2018.23.4.503

Image Restoration using GAN  

Moon, ChanKyoo (Yonsei University Department of Computer Science)
Uh, YoungJung (Yonsei University Department of Computer Science)
Byun, Hyeran (Yonsei University Department of Computer Science)
Publication Information
Journal of Broadcast Engineering / v.23, no.4, 2018 , pp. 503-510 More about this Journal
Abstract
Restoring of damaged images is a fundamental problem that was attempted before digital image processing technology appeared. Various algorithms for reconstructing damaged images have been introduced. However, the results show inferior restoration results compared with manual restoration. Recent developments of DNN (Deep Neural Network) have introduced various studies that apply it to image restoration. However, if the wide area is damaged, it can not be solved by a general interpolation method. In this case, it is necessary to reconstruct the damaged area through contextual information of surrounding images. In this paper, we propose an image restoration network using a generative adversarial network (GAN). The proposed system consists of image generation network and discriminator network. The proposed network is verified through experiments that it is possible to recover not only the natural image but also the texture of the original image through the inference of the damaged area in restoring various types of images.
Keywords
image restoration; inpainting; Computer vision; deep learning; convolutional neural network; generative adversarial network;
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