Image Inpainting Using Generative Adversarial Networks

被引：0
|
作者：
Luo H.-L. [1 ]
Ao Y. [1 ]
Yuan P. [1 ]
机构：
[1] School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2020年 / 48卷 / 10期
关键词：
Generative adversarial networks; Image inpainting; Partial convolution; Residual network;
D O I：
10.3969/j.issn.0372-2112.2020.10.003
中图分类号：
学科分类号：
摘要：
In recent years,deep learning based methods have shown preferable results for the task of inpainting corrupted images.However,the existing standard convolutional neural network approaches often cause problems with excessive color discrepancy,image texture loss and distortion.A deep network based image inpainting model is proposed in this paper,consisting of two generative adversarial network modules.One of the modules is used to inpaint the missing area of the image,where the generator is constituted with partial convolutions.The other module is the image optimization network,which is applied to solve the problem of local chromatic aberration after image restoration,and in which the generator is originated from the depth residual network.These two modules cooperated to improve the visual effect and image quality of inpainted images.Using MOS,SSIM and PSRN as the evaluation criteria,the experimental results of qualitative and quantitative comparisons with other state-of-the-art methods have shown that the proposed model performed better. © 2020, Chinese Institute of Electronics. All right reserved.
引用
收藏
页码：1891 / 1898
页数：7
相关论文
共 26 条
  • [1] Newson A, Almansa A, Fradet M, Et al., Video inpainting of complex scenes[J], Siam Journal on Imaging Sciences, 7, 4, pp. 1993-2019, (2014)
  • [2] Levin A, Zomet A, Peleg S, Et al., Seamless image stitching in the gradient domain, European Conference on Computer Vision, pp. 377-389, (2004)
  • [3] Barnes C, Et al., Patch match:A randomized correspondence algorithm for structural image editing[J], Acm Transactions on Graphics, 28, 3, pp. 2-11, (2009)
  • [4] Zheng C, Cham T, Cai J., Pluralistic image completion, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1438-1447, (2019)
  • [5] Iizuka S, Simo-Serra E, Ishikawa H., Globally and locally consistent image completion[J], ACM Transactions on Graphics, 36, 4, pp. 1-14, (2017)
  • [6] Yu J, Lin Z, Yang J, Et al., Generative image inpainting with contextual attention, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5505-5514, (2018)
  • [7] Ronneberger O, Fischer P, Brox T, Et al., U-Net:convolutional networks for biomedical image segmentation[J], Siam Journal on Imaging Sciences, 18, 4, pp. 55-67, (2015)
  • [8] Liu G, Reda A, Shih K, Et al., Image inpainting for irregular holes using partial convolutions[J], ACM Transactions on Graphics, 9, 3, pp. 37-51, (2018)
  • [9] Bertalm Marcelo, Sapiro G, Caselles V, Et al., Image inpainting, Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, pp. 417-424, (2000)
  • [10] Ballester C, Coloma M, Bertalmio, Et al., Filling-in by joint interpolation of vector fields and gray levels[J], IEEE Transactions on Image Processing, 10, 8, pp. 1200-1211, (2001)
123