Research on image Inpainting algorithm of improved GAN based on two-discriminations networks-Reference-Cited by-同舟云学术

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Research on image Inpainting algorithm of improved GAN based on two-discriminations networks

Published:2020-11-13 Issue:6 Volume:51 Page:3460-3474
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Chen Yuantao,Zhang Haopeng,Liu Linwu,Chen Xi,Zhang Qian,Yang Kai,Xia Runlong,Xie Jingbo

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province of China

the Scientific Research Fund of Hunan Provincial Education Department

the Junior Faculty Development Program Project of Changsha University of Science and Technology

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-020-01971-2.pdf

Reference43 articles.

1. Bertalmio M; Sapiro G; Caselles V (2000). Image Inpainting. In Proceedings of Annual Conference on Computer Graphics and Interactive Techniques, New Orleans, LA, USA, 23–28 ; pp. 417–424

2. Telea A (2004) An image Inpainting technique based on the fast matching method. J Graphic Tools 9(1):23–24

3. Tang F; Ying Y; Wang J; Peng QS (2004). A novel texture synthesis based algorithm for object removal in photographs. Springer, Berlin Heidelberg

4. Chen YT; Wang J; Liu SJ; Chen X; Xiong J; Xie JB; Yang K (2019). The multi-scale fast correlation filtering tracking algorithm based on a features fusion model. Concurr Comput-Pract Exp. https://doi.org/10.1002/cpe.5533

5. Barnes C, Dan BG, Shechtman E, Finkelstein A (2011) The PatchMatch randomized matching algorithm for image manipulation. Commun ACM 54(11):103–110

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