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The latest methods based on deep learning have achieved amazing results regarding the complex work of inpainting large missing areas in an image.
1901
Earlier work this paper cites.
A.A. Efros, and T.K. Leung, “Texture synthesis by non-parametric sampling,” In Proceedings of the seventh IEEE international conference on computer vision, vol. 2, pp. 1033-1038, Sep. 1999
1999
Earlier work this paper cites.
C. Ballester, M. Bertalmio, V. Caselles, G. Sapiro, and J. Verdera, “Filling-in by joint interpolation of vector fields and gray levels,” IEEE transactions on image processing, vol.10, no.8, pp.1200-1211, 2001
2001
Earlier work this paper cites.
M. Bertalmio, L. Vese, G. Sapiro, and S. Osher, “Simultaneous structure and texture image inpainting,” IEEE transactions on image processing, vol.12, no.8, pp. 882-889, 2003
2003
Earlier work this paper cites.
A. Levin, A. Zomet, and Y. Weiss, “Learning how to inpaint from global image statistics,” IEEE, 2003, pp. 305
2003
Earlier work this paper cites.
A. Telea, “An image inpainting technique based on the fast marching method,” Journal of graphics tools, vol.9, no.1, pp.23-34, 2004
2004
Earlier work this paper cites.
Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing, vol.13, no. 4, pp.600-612, 2004
2004
Earlier work this paper cites.
X. Shao, Z. Liu, and H. Li, “An image inpainting approach based on the poisson equation,” In Second International Conference on Document Image Analysis for Libraries (DIAL’06), pp. 5-pp, Apr. 2006
2006
Earlier work this paper cites.
H. Ting, S. Chen, J. Liu, and X. Tang, “Image inpainting by global structure and texture propagation,” In Proceedings of the 15th ACM international conference on Multimedia, pp. 517-520, Sep. 2007
2007
Earlier work this paper cites.
Z. Xu, and J. Sun, “Image inpainting by patch propagation using patch sparsity,” IEEE transactions on image processing, vol.19, no.5, pp.1153-1165, 2010
2010
Earlier work this paper cites.
J. Xie, L. Xu, and E. Chen, “Image denoising and inpainting with deep neural networks,” In Advances in neural information processing systems, 2012, pp. 341-349
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
L. Xu, J.S. Ren, C. Liu, and J. Jia, “Deep convolutional neural network for image deconvolution,” In Advances in neural information processing systems, 2014, pp. 1790-1798
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, … and Y. Bengio, “Generative adversarial nets,” In Advances in neural information processing systems, 2014, pp. 2672-2680
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
K. Sohn, H. Lee, and X. Yan, “Learning structured output representation using deep conditional generative models,” In Advances in neural information processing systems, 2015, pp. 3483-3491
2015
Earlier work this paper cites.
C. Doersch, S. Singh, A. Gupta, J. Sivic, and A.A. Efros, “What makes Paris look like Paris?,” Communications of the ACM, vol.58, no.12, pp.103-110, 2015
2015
Earlier work this paper cites.
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A.A. Efros, “Context encoders: Feature learning by inpainting,” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2536-2544
2016
Cited alongside, same era.
T. Zhou, S. Tulsiani, W. Sun, J. Malik, and A.A. Efros, “View synthesis by appearance flow,” In European conference on computer vision, 2016, pp. 286-301
2016
Cited alongside, same era.
J. Walker, C. Doersch, A. Gupta, and M. Hebert, “An uncertain future: Forecasting from static images using variational autoencoders,” In European Conference on Computer Vision, 2016, pp. 835-851
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Z. Yan, X. Li, M. Li, W. Zuo, and S. Shan, “Shift-net: Image inpainting via deep feature rearrangement,” In Proceedings of the European conference on computer vision, 2018, pp. 1-17
2018
Later among the works it cites.
Y. Wang, X. Tao, X. Qi, X. Shen, and J. Jia, “Image inpainting via generative multi-column convolutional neural networks,” In Advances in neural information processing systems, 2018, pp. 331-340
2018
Later among the works it cites.
J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T.S. Huang, “Generative image inpainting with contextual attention,” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 5505-5514
2018
Later among the works it cites.
Y. Song, C. Yang, Z. Lin, X. Liu, Q. Huang, H. Li, and C.C. Jay Kuo, “Contextual-based image inpainting: Infer, match, and translate,” In Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 3-19
2018
Later among the works it cites.
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2016
Cited alongside, same era.
R.Y. Zhang, P. Isola, and A.A. Efros, “Colorful image colorization,” In Computer Vision-14th European Conference, ECCV 2016, Proceedings, pp. 649-666
2016
Cited alongside, same era.
J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” In European conference on computer vision, 2016, pp. 694-711
2016
Cited alongside, same era.
J. Bao, D. Chen, F. Wen, H. Li, and G. Hua, “CVAE-GAN: fine-grained image generation through asymmetric training,” In Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 2745-2754
2017
Cited alongside, same era.
P. Isola, J.Y. Zhu, T. Zhou, and A.A. Efros, “Image-to-image translation with conditional adversarial networks,” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1125-1134
2017
Cited alongside, same era.
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba, “Places: A 10 million image database for scene recognition,” IEEE transactions on pattern analysis and machine intelligence, vol.40, no.6, pp.1452-1464, 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
S. Iizuka, E. Simo-Serra, and H. Ishikawa, “Globally and locally consistent image completion,” ACM Transactions on Graphics (ToG), vol.36, no.4, pp.1-14, 2017
2017
Cited alongside, same era.
2018
Later among the works it cites.
Y. Choi, M. Choi, M. Kim, J.W. Ha, S. Kim, and J. Choo, “Stargan: Unified generative adversarial networks for multi-domain image-to-image translation,” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 8789-8797
2018
Later among the works it cites.
R. Zhang, P. Isola, A.A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 586-595
2018
Later among the works it cites.
Z. Chen, H. Cai, Y. Zhang, C. Wu, M. Mu, Z. Li, M. A. Sotelo, “A novel sparse representation model for pedestrian abnormal trajectory understanding,” Expert Systems with Applications, vol.138, pp.112753, 2019
2019
Closest in time.
H. Liu, B. Jiang, Y. Xiao, and C. Yang, “Coherent semantic attention for image inpainting,” In Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 4170-4179
2019
Closest in time.
J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T.S. Huang, “Free-form image inpainting with gated convolution,” In Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 4471-4480
2019
Closest in time.
M.C. Sagong, Y. G. Shin, S.W. Kim, S. Park, and S.J. Ko, “Pepsi: Fast image inpainting with parallel decoding network,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2019, pp. 11360-11368
2019
Closest in time.
Y. Zeng, J. Fu, H. Chao, and B. Guo, “Learning pyramid-context encoder network for high-quality image inpainting,” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2019, pp. 1486-1494
2019
Closest in time.
Z. Huang, X. Xu, J. Ni, H. Zhu, C.Wang, “Multimodal representation learning for recommendation in Internet of Things,” IEEE Internet of Things Journal, vol.6, no.6, pp.10675-10685, 2019
2019
Closest in time.
B. Wu, T. Cheng, T.L. Yip, Y. Wang, “Fuzzy logic based dynamic decision-making system for intelligent navigation strategy within inland traffic separation schemes,” Ocean Engineering, vol.197, pp.106909, 2020
2020
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Z. Huang, X. Xu, H. Zhu, M. Zhou, “An Efficient Group Recommendation Model With Multiattention-Based Neural Networks,” IEEE Transactions on Neural Networks and Learning Systems, 2020
2020
Closest in time.
M. Jaderberg, K. Simonyan, and A. Zisserman, “Spatial transformer networks,” In Advances in neural information processing systems, 2015, pp. 2017-2025
2025
Closest in time.