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We present DeblurGAN, an end-to-end learned method for motion deblurring.
Removing camera shake from a single photograph
R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman · 2006
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Optimal Transport: Old and New
C. Villani · 2008
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Single image deblurring using motion density functions
A. Gupta, N. Joshi, C. L. Zitnick, M. Cohen, and B. Curless · 2010
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Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
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Computer Vision: Algorithms and Applications
R. Szeliski · 2010
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Non-uniform deblurring for shaken images
O. Whyte, J. Sivic, A. Zisserman, and J. Ponce · 2010
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Two-phase kernel estimation for robust motion deblurring
L. Xu and J. Jia · 2010
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Fast removal of non-uniform camera shake
M. Hirsch, C. J. Schuler, S. Harmeling, and B. Scholkopf · 2011
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Bayesian blind deconvolution with general sparse image priors
S. D. Babacan, R. Molina, M. N. Do, and A. K. Katsaggelos · 2012
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Modeling the performance of image restoration from motion blur
G. Boracchi and A. Foi · 2012
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Recording and playback of camera shake: Benchmarking blind deconvolution with a real-world database
R. Köhler, M. Hirsch, B. Mohler, B. Schölkopf, and S. Harmeling · 2012
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Unnatural L0 Sparse Representation for Natural Image Deblurring
L. Xu, S. Zheng, and J. Jia · 2013
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2013
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Generative Adversarial Networks
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
Cited alongside, same era.
Total variation blind deconvolution: The devil is in the details
D. Perrone and P. Favaro · 2014
Cited alongside, same era.
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Cited alongside, same era.
Deep convolutional neural network for image deconvolution
L. Xu, J. S. J. Ren, C. Liu, and J. Jia · 2014
Cited alongside, same era.
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi · 2016
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Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
C. Li and M. Wand · 2016
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Least squares generative adversarial networks, 2016
X. Mao, Q. Li, H. Xie, R. Y. K. Lau, and Z. Wang · 2016
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Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring
S. Nah, T. Hyun, K. Kyoung, and M. Lee · 2016
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Improved Techniques for Training GANs
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
You Only Look Once: Unified, Real-Time Object Detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2015
Cited alongside, same era.
U-Net: Convolutional Networks for Biomedical Image Segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Cited alongside, same era.
Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal
J. Sun, W. Cao, Z. Xu, and J. Ponce · 2015
Cited alongside, same era.
Empirical evaluation of rectified activations in convolutional network
B. Xu, N. Wang, T. Chen, and M. Li · 2015
Cited alongside, same era.
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan · 2016
Cited alongside, same era.
A neural approach to blind motion deblurring
A. Chakrabarti · 2016
Cited alongside, same era.
D. Ulyanov, A. Vedaldi, and V. S. Lempitsky · 2016
Later among the works it cites.
Semantic image inpainting with perceptual and contextual losses
R. A. Yeh, C. Chen, T. Lim, M. Hasegawa-Johnson, and M. N. Do · 2016
Later among the works it cites.
Wasserstein GAN
M. Arjovsky, S. Chintala, and L. Bottou · 2017
Closest in time.
Improved Training of Wasserstein GANs
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville · 2017
Closest in time.
Densely connected convolutional networks
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger · 2017
Closest in time.
An All-in-One Network for Dehazing and Beyond
B. Li, X. Peng, Z. Wang, J. Xu, and D. Feng · 2017
Closest in time.
Motion Deblurring in the Wild
M. Noroozi, P. Chandramouli, and P. Favaro · 2017
Closest in time.
Deep generative filter for motion deblurring
S. Ramakrishnan, S. Pachori, A. Gangopadhyay, and S. Raman · 2017
Closest in time.
Enhancenet: Single image super-resolution through automated texture synthesis
M. S. M. Sajjadi, B. Schölkopf, and M. Hirsch · 2017
Closest in time.
Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
Closest in time.