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Image deblurring is an ill-posed problem with multiple plausible solutions for a given input image.
Total variation blind deconvolution
Tony F Chan and Chiu-Kwong Wong · 1998
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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Removing camera shake from a single photograph
Rob Fergus, Barun Singh, Aaron Hertzmann, Sam T Roweis, and William T Freeman · 2006
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High-quality motion deblurring from a single image
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Making a “completely blind” image quality analyzer
Anish Mittal, Rajiv Soundararajan, and Alan C Bovik · 2012
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Deconvolving psfs for a better motion deblurring using multiple images
Xiang Zhu, Filip Šroubek, and Peyman Milanfar · 2012
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Unnatural l0 sparse representation for natural image deblurring
Li Xu, Shicheng Zheng, and Jiaya Jia · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Removing camera shake via weighted fourier burst accumulation
Mauricio Delbracio and Guillermo Sapiro · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Learning a convolutional neural network for non-uniform motion blur removal
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A neural approach to blind motion deblurring
Ayan Chakrabarti · 2016
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Image style transfer using convolutional neural networks
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2016
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A comparative study for single image blind deblurring
Wei-Sheng Lai, Jia-Bin Huang, Zhe Hu, Narendra Ahuja, and Ming-Hsuan Yang · 2016
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Instance normalization: The missing ingredient for fast stylization
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Ntire 2017 challenge on single image super-resolution: Dataset and study
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee · 2017
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Deep multi-scale convolutional neural network for dynamic scene deblurring
Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee · 2017
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Deep generative filter for motion deblurring
Sainandan Ramakrishnan, Shubham Pachori, Aalok Gangopadhyay, and Shanmuganathan Raman · 2017
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Deep video deblurring for hand-held cameras
Shuochen Su, Mauricio Delbracio, Jue Wang, Guillermo Sapiro, Wolfgang Heidrich, and Oliver Wang · 2017
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Learning blind motion deblurring
Patrick Wieschollek, Michael Hirsch, Bernhard Scholkopf, and Hendrik Lensch · 2017
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Demystifying mmd gans
Mikołaj Bińkowski, Danica J Sutherland, Michael Arbel, and Arthur Gretton · 2018
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The perception-distortion tradeoff
Yochai Blau and Tomer Michaeli · 2018
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
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Distribution matching losses can hallucinate features in medical image translation
Joseph Paul Cohen, Margaux Luck, and Sina Honari · 2018
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Normalized blind deconvolution
Meiguang Jin, Stefan Roth, and Paolo Favaro · 2018
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Deblurgan: Blind motion deblurring using conditional adversarial networks
Orest Kupyn, Volodymyr Budzan, Mykola Mykhailych, Dmytro Mishkin, and Jiří Matas · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2018
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Wavegrad 2: Iterative refinement for text-to-speech synthesis
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, Najim Dehak, and William Chan · 2021
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Rethinking coarse-to-fine approach in single image deblurring
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Projected distribution loss for image enhancement
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The unreasonable effectiveness of deep features as a perceptual metric
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