Fetching the paper…
Reading the bibliography…
While deep learning-based methods for blind face restoration have achieved unprecedented success, they still suffer from two major limitations.
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.
C. M. Bishop and N. M. Nasrabadi, Pattern recognition and machine learning . Springer, 2006, vol. 4, no. 4
2006
Earlier work this paper cites.
G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database forstudying face recognition in unconstrained environments,” in Workshop on faces in ’Real-Life’ Images: detection, alignment, and recognition , 2008
2008
Earlier work this paper cites.
A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a “completely blind” image quality analyzer,” IEEE Signal Processing Letters , vol. 20, no. 3, pp. 209–212, 2012
2012
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 Proceedings of Advances in Neural Information Processing Systems (NeurIPS) , 2014
2014
Earlier work this paper cites.
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International Conference on Machine Learning (ICML) , 2015, pp. 2256–2265
2015
Earlier work this paper cites.
C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 38, no. 2, pp. 295–307, 2015
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proceedings of International Conference on Learning Representations (ICLR) , 2015
2015
Earlier work this paper cites.
J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2016, pp. 694–711
2016
Earlier work this paper cites.
S. Yang, P. Luo, C.-C. Loy, and X. Tang, “Wider face: A face detection benchmark,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 5525–5533
2016
Earlier work this paper cites.
W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 1874–1883
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778
2016
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “SGDR: stochastic gradient descent with warm restarts,” in Proceedings of International Conference on Learning Representations (ICLR) , 2017
2017
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “GANs trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in Neural Information Processing Systems (NeurIPS , vol. 30, 2017
2017
Earlier work this paper cites.
C. Ma, C.-Y. Yang, X. Yang, and M.-H. Yang, “Learning a no-reference quality metric for single-image super-resolution,” Computer Vision and Image Understanding , vol. 158, pp. 1–16, 2017
2017
Earlier work this paper 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/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 586–595
2018
Earlier work this paper cites.
Y. Chen, Y. Tai, X. Liu, C. Shen, and J. Yang, “FSRNet: End-to-end learning face super-resolution with facial priors,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 2492–2501
2018
Earlier work this paper cites.
Z. Shen, W.-S. Lai, T. Xu, J. Kautz, and M.-H. Yang, “Deep semantic face deblurring,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 8260–8269
2018
Earlier work this paper cites.
X. Li, M. Liu, Y. Ye, W. Zuo, L. Lin, and R. Yang, “Learning warped guidance for blind face restoration,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 272–289
2018
Earlier work this paper cites.
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of GANs for improved quality, stability, and variation,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2018
2018
Earlier work this paper cites.
Y. Blau, R. Mechrez, R. Timofte, T. Michaeli, and L. Zelnik-Manor, “The 2018 PIRM challenge on perceptual image super-resolution,” in Proceedings of the European Conference on Computer Vision Workshops (ECCV-W) , 2018, pp. 0–0
2018
Earlier work this paper cites.
C. Yu, J. Wang, C. Peng, C. Gao, G. Yu, and N. Sang, “Bisenet: Bilateral segmentation network for real-time semantic segmentation,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 325–341
2018
Earlier work this paper 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/CVF International Conference on Computer Vision (ICCV) , 2019, pp. 9388–9397
2019
Earlier work this paper cites.
B. Dogan, S. Gu, and R. Timofte, “Exemplar guided face image super-resolution without facial landmarks,” in Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (CVPR-W) , 2019, pp. 0–0
2019
Earlier work this paper cites.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 4401–4410
2019
Earlier work this paper cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al. , “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (NeurIPS) , vol. 32, 2019
2019
Cited alongside, same era.
J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “ArcFace: additive angular margin loss for deep face recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 4690–4699
2019
Cited alongside, same era.
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/CVF International Conference on Computer Vision (ICCV) , 2019, pp. 4471–4480
2019
Cited alongside, same era.
X. Li, C. Chen, S. Zhou, X. Lin, W. Zuo, and L. Zhang, “Blind face restoration via deep multi-scale component dictionaries,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2020, pp. 399–415
K. C. Chan, X. Wang, X. Xu, J. Gu, and C. C. Loy, “GLEAN: generative latent bank for large-factor image super-resolution,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022
2022
Closest in time.
S. Zhou, K. C. K. Chan, C. Li, and C. C. Loy, “Towards robust blind face restoration with codebook lookup transformer,” Proceedings of Advances in Neural Information Processing Systems (NeurIPS) , 2022
2022
Closest in time.
C. Saharia, J. Ho, W. Chan, T. Salimans, D. J. Fleet, and M. Norouzi, “Image super-resolution via iterative refinement,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022
2022
Closest in time.
F. Zhu, J. Zhu, W. Chu, X. Zhang, X. Ji, C. Wang, and Y. Tai, “Blind face restoration via integrating face shape and generative priors,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 7662–7671
2022
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 33, pp. 6840–6851, 2020
2020
Cited alongside, same era.
X. Hu, W. Ren, J. LaMaster, X. Cao, X. Li, Z. Li, B. Menze, and W. Liu, “Face super-resolution guided by 3D facial priors,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2020, pp. 763–780
2020
Cited alongside, same era.
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 (CVPR) , 2020, pp. 2437–2445
2020
Cited alongside, same era.
J. Gu, Y. Shen, and B. Zhou, “Image processing using multi-code GAN prior,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 3012–3021
2020
Cited alongside, same era.
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differential equations,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2020
2020
Cited alongside, same era.
X. Wang, Y. Li, H. Zhang, and Y. Shan, “Towards real-world blind face restoration with generative facial prior,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 9168–9178
2021
Cited alongside, same era.
X. Tu, J. Zhao, Q. Liu, W. Ai, G. Guo, Z. Li, W. Liu, and J. Feng, “Joint face image restoration and frontalization for recognition,” IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) , vol. 32, no. 3, pp. 1285–1298, 2021
2021
Cited alongside, same era.
X. Pan, X. Zhan, B. Dai, D. Lin, C. C. Loy, and P. Luo, “Exploiting deep generative prior for versatile image restoration and manipulation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021
2021
Cited alongside, same era.
W. Xia, Y. Zhang, Y. Yang, J.-H. Xue, B. Zhou, and M.-H. Yang, “GAN inversion: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022
2022
Closest in time.
Z. Wang, J. Zhang, R. Chen, W. Wang, and P. Luo, “RestoreFormer: high-quality blind face restoration from undegraded key-value pairs,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 17 512–17 521
2022
Closest in time.
H. Li, Y. Yang, M. Chang, S. Chen, H. Feng, Z. Xu, Q. Li, and Y. Chen, “SRDiff: single image super-resolution with diffusion probabilistic models,” Neurocomputing , vol. 479, pp. 47–59, 2022
2022
Closest in time.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 10 684–10 695
2022
Closest in time.
A. Lugmayr, M. Danelljan, A. Romero, F. Yu, R. Timofte, and L. Van Gool, “Repaint: Inpainting using denoising diffusion probabilistic models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022, pp. 11 461–11 471
2022
Closest in time.
B. Kawar, M. Elad, S. Ermon, and J. Song, “Denoising diffusion restoration models,” in Proceedings of Advances in Neural Information Processing Systems (NeurIPS) , vol. 35, 2022, pp. 23 593–23 606
2022
Closest in time.
H. Chung, B. Sim, and J. C. Ye, “Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 12 413–12 422
2022
Closest in time.
H. Chung, B. Sim, D. Ryu, and J. C. Ye, “Improving diffusion models for inverse problems using manifold constraints,” in Proceedings of Advances in Neural Information Processing Systems (NeurIPS) , vol. 35, 2022, pp. 25 683–25 696
2022
Closest in time.
R. Suvorov, E. Logacheva, A. Mashikhin, A. Remizova, A. Ashukha, A. Silvestrov, N. Kong, H. Goka, K. Park, and V. Lempitsky, “Resolution-robust large mask inpainting with fourier convolutions,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , 2022, pp. 2149–2159
2022
Closest in time.
J. Song, A. Vahdat, M. Mardani, and J. Kautz, “Pseudoinverse-guided diffusion models for inverse problems,” in Proceedings of International Conference on Learning Representations (ICLR) , 2022
2022
Closest in time.
K. Mei and V. M. Patel, “LTT-GAN:: Looking through turbulence by inverting gans,” IEEE Journal of Selected Topics in Signal Processing , 2023
2023
Closest in time.
Y. Zhao, T. Hou, Y.-C. Su, X. Jia, Y. Li, and M. Grundmann, “Towards authentic face restoration with iterative diffusion models and beyond,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2023, pp. 7312–7322
2023
Closest in time.
B. Fei, Z. Lyu, L. Pan, J. Zhang, W. Yang, T. Luo, B. Zhang, and B. Dai, “Generative diffusion prior for unified image restoration and enhancement,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2023, pp. 9935–9946
2023
Closest in time.
Z. Wang, Z. Zhang, X. Zhang, H. Zheng, M. Zhou, Y. Zhang, and Y. Wang, “Dr2: Diffusion-based robust degradation remover for blind face restoration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 1704–1713
2023
Closest in time.
Y. Wang, J. Yu, and J. Zhang, “Zero-shot image restoration using denoising diffusion null-space model,” in Proceedings of International Conference on Learning Representations (ICLR) , 2023
2023
Closest in time.
Y. Zhu, K. Zhang, J. Liang, J. Cao, B. Wen, R. Timofte, and L. Van Gool, “Denoising diffusion models for plug-and-play image restoration,” in Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (CVPR-W) , 2023, pp. 1219–1229
2023
Closest in time.
Z. Yue, J. Wang, and C. C. Loy, “Resshift: Efficient diffusion model for image super-resolution by residual shifting,” in Proceedings of Advances in Neural Information Processing Systems (NeurIPS) , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
X. Qiu, C. Han, Z. Zhang, B. Li, T. Guo, and X. Nie, “DiffBFR: Bootstrapping diffusion model for blind face restoration,” in Proceedings of the ACM International Conference on Multimedia (ACM MM) , 2023, pp. 7785–7795
2023
Closest in time.