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Blind deconvolution is a classical yet challenging low-level vision problem with many real-world applications.
Learning iteration-wise generalized shrinkage–thresholding operators for blind deconvolution
W. Zuo, D. Ren, D. Zhang, S. Gu, and L. Zhang · 1908
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Total variation blind deconvolution
T. F. Chan and C.-K. Wong · 1998
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Convergence of a block coordinate descent method for nondifferentiable minimization
P. Tseng · 2001
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Image quality assessment: from error visibility to structural similarity
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli · 2004
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Fast motion deblurring
S. Cho and S. Lee · 2009
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Fast image deconvolution using
D. Krishnan and R. Fergus · 2009
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Understanding and evaluating blind deconvolution algorithms
A. Levin, Y. Weiss, F. Durand, and W. T. Freeman · 2009
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Scale invariance and noise in natural images
D. Zoran and Y. Weiss · 2009
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Two-phase kernel estimation for robust motion deblurring
L. Xu and J. Jia · 2010
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Blind deconvolution using a normalized sparsity measure
D. Krishnan, T. Tay, and R. Fergus · 2011
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Efficient marginal likelihood optimization in blind deconvolution
A. Levin, Y. Weiss, F. Durand, and W. T. Freeman · 2011
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From learning models of natural image patches to whole image restoration
D. Zoran and Y. Weiss · 2011
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Nonlocally centralized sparse representation for image restoration
W. Dong, L. Zhang, G. Shi, and X. Li · 2013
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Discriminative non-blind deblurring
U. Schmidt, C. Rother, S. Nowozin, J. Jancsary, and S. Roth · 2013
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Edge-based blur kernel estimation using patch priors
L. Sun, S. Cho, J. Wang, and J. Hays · 2013
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Unnatural l0 sparse representation for natural image deblurring
L. Xu, S. Zheng, and J. Jia · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Blind image deblurring using spectral properties of convolution operators
G. Liu, S. Chang, and Y. Ma · 2014
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Blind deblurring using internal patch recurrence
T. Michaeli and M. Irani · 2014
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Total variation blind deconvolution: The devil is in the details
D. Perrone and P. Favaro · 2014
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Shrinkage fields for effective image restoration
U. Schmidt and S. Roth · 2014
Cited alongside, same era.
Deblurring shaken and partially saturated images
O. Whyte, J. Sivic, and A. Zisserman · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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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.
Image deblurring via extreme channels prior
Y. Yan, W. Ren, Y. Guo, R. Wang, and X. Cao · 2017
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Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang · 2017
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Learning deep cnn denoiser prior for image restoration
K. Zhang, W. Zuo, S. Gu, and L. Zhang · 2017
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Normalized blind deconvolution
M. Jin, S. Roth, and P. Favaro · 2018
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Deblurgan: Blind motion deblurring using conditional adversarial networks
O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, and J. Matas · 2018
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Learning a discriminative prior for blind image deblurring
L. Li, J. Pan, W.-S. Lai, C. Gao, N. Sang, and M.-H. Yang · 2018
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A neural approach to blind motion deblurring
A. Chakrabarti · 2016
Cited alongside, same era.
Accurate image super-resolution using very deep convolutional networks
J. Kim, J. Kwon Lee, and K. Mu Lee · 2016
Cited alongside, same era.
Deeply-recursive convolutional network for image super-resolution
J. Kim, J. Kwon Lee, and K. Mu Lee · 2016
Cited alongside, same era.
A comparative study for single image blind deblurring
W.-S. Lai, J.-B. Huang, Z. Hu, N. Ahuja, and M.-H. Yang · 2016
Cited alongside, same era.
Image deblurring via enhanced low-rank prior
W. Ren, X. Cao, J. Pan, X. Guo, W. Zuo, and M.-H. Yang · 2016
Cited alongside, same era.
Learning to deblur
C. J. Schuler, M. Hirsch, S. Harmeling, and B. Schölkopf · 2016
Cited alongside, same era.
Learning to deblur images with exemplars
J. Pan, W. Ren, Z. Hu, and M.-H. Yang · 2018
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Deblurring images via dark channel prior
J. Pan, D. Sun, H. Pfister, and M.-H. Yang · 2018
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¡°zero-shot¡± super-resolution using deep internal learning
A. Shocher, N. Cohen, and M. Irani · 2018
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Scale-recurrent network for deep image deblurring
X. Tao, H. Gao, X. Shen, J. Wang, and J. Jia · 2018
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Deep image prior
D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2018
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Dynamic scene deblurring using spatially variant recurrent neural networks
J. Zhang, J. Pan, J. Ren, Y. Song, L. Bao, R. W. Lau, and M.-H. Yang · 2018
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Blind image deblurring with local maximum gradient prior
L. Chen, F. Fang, T. Wang, and G. Zhang · 2019
Closest in time.
” double-dip”: Unsupervised image decomposition via coupled deep-image-priors
Y. Gandelsman, A. Shocher, and M. Irani · 2019
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Dynamic scene deblurring with parameter selective sharing and nested skip connections
H. Gao, X. Tao, X. Shen, and J. Jia · 2019
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On the convergence of learning-based iterative methods for nonconvex inverse problems
R. Liu, S. Cheng, Y. He, X. Fan, Z. Lin, and Z. Luo · 2019
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Recurrent neural networks with intra-frame iterations for video deblurring
S. Nah, S. Son, and K. M. Lee · 2019
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Simultaneous fidelity and regularization learning for image restoration
D. Ren, W. Zuo, D. Zhang, L. Zhang, and M.-H. Yang · 2019
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