Masked Image Inpainting Algorithm Based on Generative Adversarial Nets

doi:10.13190/j.jbupt.2017-247

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2018, Vol. 41 ›› Issue (3): 81-86.doi: 10.13190/j.jbupt.2017-247

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Masked Image Inpainting Algorithm Based on Generative Adversarial Nets

CAO Zhi-yi, NIU Shao-zhang, ZHANG Ji-wei

Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2017-12-04 Online:2018-06-28 Published:2018-06-04

Abstract

Abstract: A masked image inpainting algorithm based on generative adversarial nets was proposed, which can restore the original image from the lacking of a large number of pixels. Unlike other block search restoration algorithms, the algorithm proposed directly generates possible missing elements and restore them. Due to the improved structure of generated model and the calculation method of generating loss on generative adversarial nets, this article has the characteristics of semi-supervised learning. Experiments show that the proposed method outperforms the existing one on the premise of satisfying the overall contour of the image.

Key words: generative adversarial nets, semisupervised learning, masked image inpainting

CLC Number:

TN929.53

CAO Zhi-yi, NIU Shao-zhang, ZHANG Ji-wei. Masked Image Inpainting Algorithm Based on Generative Adversarial Nets[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(3): 81-86.

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Masked Image Inpainting Algorithm Based on Generative Adversarial Nets

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