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We propose to restore old photos that suffer from severe degradation through a deep learning approach.
F. Stanco, G. Ramponi, and A. De Polo, “Towards the automated restoration of old photographic prints: a survey,” in The IEEE Region 8 EUROCON 2003. Computer as a Tool. , vol. 2. IEEE, 2003, pp. 370–374
2003
Earlier work this paper cites.
V. Bruni and D. Vitulano, “A generalized model for scratch detection,” IEEE transactions on image processing , vol. 13, no. 1, pp. 44–50, 2004
2004
Earlier work this paper cites.
R.-C. Chang, Y.-L. Sie, S.-M. Chou, and T. K. Shih, “Photo defect detection for image inpainting,” in Seventh IEEE International Symposium on Multimedia (ISM’05) . IEEE, 2005, pp. 5–pp
2005
Earlier work this paper cites.
I. Giakoumis, N. Nikolaidis, and I. Pitas, “Digital image processing techniques for the detection and removal of cracks in digitized paintings,” IEEE Transactions on Image Processing , vol. 15, no. 1, pp. 178–188, 2005
2005
Earlier work this paper cites.
A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denoising,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) , vol. 2. IEEE, 2005, pp. 60–65
2005
Earlier work this paper cites.
M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Transactions on Image processing , vol. 15, no. 12, pp. 3736–3745, 2006
2006
Earlier work this paper cites.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Transactions on image processing , vol. 16, no. 8, pp. 2080–2095, 2007
2007
Earlier work this paper cites.
J. Mairal, M. Elad, and G. Sapiro, “Sparse representation for color image restoration,” IEEE Transactions on image processing , vol. 17, no. 1, pp. 53–69, 2007
2007
Earlier work this paper cites.
Y. Weiss and W. T. Freeman, “What makes a good model of natural images?” in 2007 IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 2007, pp. 1–8
2007
Earlier work this paper cites.
S. D. Babacan, R. Molina, and A. K. Katsaggelos, “Total variation super resolution using a variational approach,” in 2008 15th IEEE International Conference on Image Processing . IEEE, 2008, pp. 641–644
2008
Earlier work this paper cites.
J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, “Non-local sparse models for image restoration,” in 2009 IEEE 12th international conference on computer vision . IEEE, pp. 2272–2279
2009
Earlier work this paper cites.
S. Z. Li, Markov random field modeling in image analysis . Springer Science & Business Media, 2009
2009
Earlier work this paper cites.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Bm3d image denoising with shape-adaptive principal component analysis,” 2009
2009
Earlier work this paper cites.
J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE transactions on image processing , vol. 19, no. 11, pp. 2861–2873, 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.
A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Transactions on image processing , vol. 21, no. 12, pp. 4695–4708, 2012
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
Y. Hacohen, E. Shechtman, and D. Lischinski, “Deblurring by example using dense correspondence,” in Proceedings of the IEEE International Conference on Computer Vision , 2013, pp. 2384–2391
2013
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” CoRR , vol. abs/1312.6114, 2013
2013
Earlier work this paper cites.
C. Dong, C. C. Loy, K. He, and X. Tang, “Learning a deep convolutional network for image super-resolution,” in European conference on computer vision . Springer, 2014, pp. 184–199
2014
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.
J. Pan, Z. Hu, Z. Su, and M.-H. Yang, “Deblurring face images with exemplars,” in European conference on computer vision . Springer, 2014, pp. 47–62
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, 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.
J. Sun, W. Cao, Z. Xu, and J. Ponce, “Learning a convolutional neural network for non-uniform motion blur removal,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 769–777
2015
Cited alongside, same era.
M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes challenge: A retrospective,” International journal of computer vision , vol. 111, no. 1, pp. 98–136, 2015
2015
Cited alongside, same era.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
Cited alongside, same era.
W. Ren, S. Liu, H. Zhang, J. Pan, X. Cao, and M.-H. Yang, “Single image dehazing via multi-scale convolutional neural networks,” in European conference on computer vision . Springer, 2016, pp. 154–169
2016
Cited alongside, same era.
G. Liu, F. A. Reda, K. J. Shih, T.-C. Wang, A. Tao, and B. Catanzaro, “Image inpainting for irregular holes using partial convolutions,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 85–100
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.
K. Yu, C. Dong, L. Lin, and C. Change Loy, “Crafting a toolchain for image restoration by deep reinforcement learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 2443–2452
2018
Later among the works it cites.
2018
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X. Mao, C. Shen, and Y.-B. Yang, “Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections,” in Advances in neural information processing systems , 2016, pp. 2802–2810
2016
Cited alongside, same era.
J. Kim, J. Kwon Lee, and K. Mu Lee, “Accurate image super-resolution using very deep convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 1646–1654
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 . Springer, 2016, pp. 694–711
2016
Cited alongside, same era.
K. Zhang, W. Zuo, S. Gu, and L. Zhang, “Learning deep cnn denoiser prior for image restoration,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 3929–3938
2017
Cited alongside, same era.
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” IEEE Transactions on Image Processing , vol. 26, no. 7, pp. 3142–3155, 2017
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 Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on , 2017
2017
Cited alongside, same era.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang et al. , “Photo-realistic single image super-resolution using a generative adversarial network,” arXiv preprint , 2017
2017
Cited alongside, same era.
S. Nah, T. Hyun Kim, and K. Mu Lee, “Deep multi-scale convolutional neural network for dynamic scene deblurring,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 3883–3891
2017
Cited alongside, same era.
Later among the works it cites.
D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Deep image prior,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 9446–9454
2018
Later among the works it cites.
Z. Shen, W.-S. Lai, T. Xu, J. Kautz, and M.-H. Yang, “Deep semantic face deblurring,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 8260–8269
2018
Later among the works it cites.
A. Bulat and G. Tzimiropoulos, “Super-fan: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with gans,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 109–117
2018
Later among the works it cites.
X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 7794–7803
2018
Later among the works it cites.
T.-C. Wang, M.-Y. Liu, J.-Y. Zhu, A. Tao, J. Kautz, and B. Catanzaro, “High-resolution image synthesis and semantic manipulation with conditional gans,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 8798–8807
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.
B. Zhang, M. He, J. Liao, P. V. Sander, L. Yuan, A. Bermak, and D. Chen, “Deep exemplar-based video colorization,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 8052–8061
2019
Later among the works it cites.
Q. Gao, X. Shu, and X. Wu, “Deep restoration of vintage photographs from scanned halftone prints,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 4120–4129
2019
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2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
C. Chan, S. Ginosar, T. Zhou, and A. A. Efros, “Everybody dance now,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 5933–5942
2019
Later among the works it cites.
K. Grm, W. J. Scheirer, and V. Štruc, “Face hallucination using cascaded super-resolution and identity priors,” IEEE Transactions on Image Processing , vol. 29, no. 1, pp. 2150–2165, 2019
2019
Later among the works it cites.
W. Ren, J. Yang, S. Deng, D. Wipf, X. Cao, and X. Tong, “Face video deblurring using 3d facial priors,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 9388–9397
2019
Later among the works it cites.
T. Park, M.-Y. Liu, T.-C. Wang, and J.-Y. Zhu, “Semantic image synthesis with spatially-adaptive normalization,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 2337–2346
2019
Later among the works it cites.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2019, pp. 4401–4410
2019
Later among the works it cites.
K. Nazeri, E. Ng, T. Joseph, F. Qureshi, and M. Ebrahimi, “Edgeconnect: Generative image inpainting with adversarial edge learning,” 2019
2019
Later among the works it cites.
S. Menon, A. Damian, S. Hu, N. Ravi, and C. Rudin, “Pulse: Self-supervised photo upsampling via latent space exploration of generative models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 2437–2445
2020
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