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Many recent inpainting works have achieved impressive results by leveraging Deep Neural Networks (DNNs) to model various prior information for image restoration.
Bertalmío, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. Proceedings of the 27th annual conference on Computer graphics and interactive techniques (2000)
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2002
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Criminisi, A., Pérez, P., Toyama, K.: Object removal by exemplar-based inpainting. 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. 2
2003
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Levin, A., Zomet, A., Weiss, Y.: Learning how to inpaint from global image statistics. Proceedings Ninth IEEE International Conference on Computer Vision pp. 305–312 vol.1 (2003)
2003
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Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13
2004
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Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). vol. 1, pp. 886–893 vol. 1 (2005). https://doi.org/10.1109/CVPR.2005.177
2005
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Roth, S., Black, M.J.: Fields of experts: a framework for learning image priors. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) 2
2005
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Hays, J., Efros, A.A.: Scene completion using millions of photographs. ACM Transactions on Graphics (SIGGRAPH 2007) 26
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Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25
2012
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Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Advances in neural information processing systems 27
2014
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Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European conference on computer vision. pp. 740–755. Springer (2014)
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2015
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Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV) 115
2015
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Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: Dataset and study. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (July 2017)
2017
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2017
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Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1125–1134 (2017)
2017
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems. pp. 5998–6008 (2017)
2017
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Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: A 10 million image database for scene recognition. IEEE transactions on pattern analysis and machine intelligence 40
2017
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2018
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Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium (2018)
2018
Cited alongside, same era.
Radford, A., Narasimhan, K.: Improving language understanding by generative pre-training (2018)
2018
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2018
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Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional gans. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 8798–8807 (2018)
2018
Cited alongside, same era.
Zeng, Y., Lin, Z., Yang, J., Zhang, J., Shechtman, E., Lu, H.: High-resolution image inpainting with iterative confidence feedback and guided upsampling. In: European Conference on Computer Vision. pp. 1–17. Springer (2020)
2020
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Zhou, T., Ding, C., Lin, S., Wang, X., Tao, D.: Learning oracle attention for high-fidelity face completion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7680–7689 (2020)
2020
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2021
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2021
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Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., Huang, T.S.: Generative image inpainting with contextual attention. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 5505–5514 (2018)
2018
Cited alongside, same era.
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 586–595 (2018)
2018
Cited alongside, same era.
2019
Cited alongside, same era.
Jo, Y., Park, J.: Sc-fegan: Face editing generative adversarial network with user’s sketch and color. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1745–1753 (2019)
2019
Cited alongside, same era.
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4396–4405 (2019). https://doi.org/10.1109/CVPR.2019.00453
2019
Cited alongside, same era.
Nazeri, K., Ng, E., Joseph, T., Qureshi, F., Ebrahimi, M.: Edgeconnect: Structure guided image inpainting using edge prediction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (2019)
2019
Cited alongside, same era.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners (2019)
2019
Cited alongside, same era.
Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., Huang, T.S.: Free-form image inpainting with gated convolution. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4471–4480 (2019)
2019
Cited alongside, same era.
Chen, Y., Liu, S., Wang, X.: Learning continuous image representation with local implicit image function. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8628–8638 (2021)
2021
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Guo, X., Yang, H., Huang, D.: Image inpainting via conditional texture and structure dual generation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 14134–14143 (2021)
2021
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Guo, X., Yang, H., Huang, D.: Image inpainting via conditional texture and structure dual generation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 14134–14143 (October 2021)
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Yu, Y., Zhan, F., Lu, S., Pan, J., Ma, F., Xie, X., Miao, C.: Wavefill: A wavelet-based generation network for image inpainting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 14114–14123 (2021)
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2021
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Dong, Q., Cao, C., Fu, Y.: Incremental transformer structure enhanced image inpainting with masking positional encoding (2022)
2022
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Li, W., Lin, Z., Zhou, K., Qi, L., Wang, Y., Jia, J.: Mat: Mask-aware transformer for large hole image inpainting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10758–10768 (2022)
2022
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Zhai, X., Kolesnikov, A., Houlsby, N., Beyer, L.: Scaling vision transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12104–12113 (2022)
2022
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