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Deep learning methods have witnessed the great progress in image restoration with specific metrics (e.g., PSNR, SSIM).
Kodak lossless true color image suite
R. Franzen · 1999
Earlier work this paper cites.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
D. Martin, C. Fowlkes, D. Tal, and J. Malik · 2001
Earlier work this paper cites.
Unsupervised learning of image manifolds by semidefinite programming
K. Q. Weinberger and L. K. Saul · 2006
Earlier work this paper cites.
Image denoising by sparse 3-d transform-domain collaborative filtering
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian · 2007
Earlier work this paper cites.
Pointwise shape-adaptive dct for high-quality denoising and deblocking of grayscale and color images
A. Foi, V. Katkovnik, and K. Egiazarian · 2007
Earlier work this paper cites.
Visual importance pooling for image quality assessment
A. K. Moorthy and A. C. Bovik · 2009
Earlier work this paper cites.
Low-complexity single-image super-resolution based on nonnegative neighbor embedding
M. Bevilacqua, A. Roumy, C. Guillemot, and M. L. Alberi-Morel · 2012
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Better mixing via deep representations
Y. Bengio, G. Mesnil, Y. Dauphin, and S. Rifai · 2013
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Compression artifacts reduction by a deep convolutional network
C. Dong, Y. Deng, C. Change Loy, and X. Tang · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Single image super-resolution from transformed self-exemplars
J.-B. Huang, A. Singh, and N. Ahuja · 2015
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Why deep learning works: A manifold disentanglement perspective
P. P. Brahma, D. Wu, and Y. She · 2016
Earlier work this paper cites.
Image super-resolution using deep convolutional networks
C. Dong, C. C. Loy, K. He, and X. Tang · 2016
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Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
Earlier work this paper cites.
Accurate image super-resolution using very deep convolutional networks
J. Kim, J. Kwon Lee, and K. Mu Lee · 2016
Cited alongside, same era.
Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang · 2016
Cited alongside, same era.
Ntire 2017 challenge on single image super-resolution: Dataset and study
E. Agustsson and R. Timofte · 2017
Cited alongside, same era.
Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration
Y. Chen and T. Pock · 2017
Cited alongside, same era.
Improved training of wasserstein gans
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville · 2017
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
Non-local recurrent network for image restoration
D. Liu, B. Wen, Y. Fan, C. C. Loy, and T. S. Huang · 2018
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Multi-level wavelet-cnn for image restoration
P. Liu, H. Zhang, K. Zhang, L. Lin, and W. Zuo · 2018
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Attribute-guided face generation using conditional cyclegan
Y. Lu, Y.-W. Tai, and C.-K. Tang · 2018
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Maintaining natural image statistics with the contextual loss
R. Mechrez, I. Talmi, F. Shama, and L. Zelnik-Manor · 2018
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Multi–scale recursive and perception–distortion controllable image super–resolution
P. N. Michelini, D. Zhu, and H. Liu · 2018
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C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al · 2017
Cited alongside, same era.
Enhanced deep residual networks for single image super-resolution
B. Lim, S. Son, H. Kim, S. Nah, and K. Mu Lee · 2017
Cited alongside, same era.
Enhancenet: Single image super-resolution through automated texture synthesis
M. S. Sajjadi, B. Scholkopf, and M. Hirsch · 2017
Cited alongside, same era.
Image super-resolution via deep recursive residual network
Y. Tai, J. Yang, and X. Liu · 2017
Cited alongside, same era.
Memnet: A persistent memory network for image restoration
Y. Tai, J. Yang, X. Liu, and C. Xu · 2017
Cited alongside, same era.
Deep feature interpolation for image content changes
P. Upchurch, J. Gardner, G. Pleiss, R. Pless, N. Snavely, K. Bala, and K. Weinberger · 2017
Cited alongside, same era.
Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang · 2017
Cited alongside, same era.
A. Shoshan, R. Mechrez, and L. Zelnik-Manor · 2018
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Esrgan: Enhanced super-resolution generative adversarial networks
X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, Y. Qiao, and C. C. Loy · 2018
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Super-resolving very low-resolution face images with supplementary attributes
X. Yu, B. Fernando, R. Hartley, and F. Porikli · 2018
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Ffdnet: Toward a fast and flexible solution for cnn-based image denoising
K. Zhang, W. Zuo, and L. Zhang · 2018
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Image super-resolution using very deep residual channel attention networks
Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu · 2018
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Residual dense network for image super-resolution
Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu · 2018
Later among the works it cites.
Toward convolutional blind denoising of real photographs
S. Guo, Z. Yan, K. Zhang, W. Zuo, and L. Zhang · 2019
Closest in time.
A style-based generator architecture for generative adversarial networks
T. Karras, S. Laine, and T. Aila · 2019
Closest in time.
Deep network interpolation for continuous imagery effect transition
X. Wang, K. Yu, C. Dong, X. Tang, and C. C. Loy · 2019
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Lightweight feature fusion network for single image super-resolution
W. Yang, W. Wang, X. Zhang, S. Sun, and Q. Liao · 2019
Closest in time.