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Image super-resolution (SR) is an underdetermined inverse problem, where a large number of plausible high-resolution images can explain the same downsampled image.
Multiscale structural similarity for image quality assessment
Zhou Wang, Eero P Simoncelli, and Alan C Bovik · 2003
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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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Denoising source separation
Jaakko Särelä and Harri Valpola · 2005
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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Mixtures of conditional gaussian scale mixtures applied to multiscale image representations
Lucas Theis, Reshad Hosseini, and Matthias Bethge · 2012
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What regularized auto-encoders learn from the data-generating distribution
Guillaume Alain and Yoshua Bengio · 2014
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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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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Super-resolution: a comprehensive survey
Kamal Nasrollahi and Thomas B. Moeslund · 2014
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Lasagne: First release., 2015
Sander Dieleman, Jan Schlüter, Colin Raffel, Eben Olson, Søren Kaae Sønderby, Daniel Nouri, and Eric Battenberg and · 2015
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Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, and Ole Winther · 2015
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Optimal recovery from compressive measurements via denoising-based approximate message passing
Christopher A Metzler, Arian Maleki, and Richard G Baraniuk · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Semi-supervised learning with ladder networks
Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko · 2015
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Generative image modeling using spatial lstms
Lucas Theis and Matthias Bethge · 2015
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Super-resolution with deep convolutional sufficient statistics
Joan Bruna, Pablo Sprechmann, and Yann LeCun · 2016
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Image super-resolution using deep convolutional networks
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2016
Perceptual image quality assessment using a normalized laplacian pyramid
Valero Laparra, Johannes Ballé, Alexander Berardino, and Eero P Simoncelli · 2016
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Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi · 2016
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Combining markov random fields and convolutional neural networks for image synthesis
Chuan Li and Michael Wand · 2016
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Learning in implicit generative models
Shakir Mohamed and Balaji Lakshminarayanan · 2016
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f-GAN: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Generating images with perceptual similarity metrics based on deep networks
Alexey Dosovitskiy and Thomas Brox · 2016
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Open source code
David Garcia · 2016
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Tagger: Deep unsupervised perceptual grouping
Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hotloo Hao, Jürgen Schmidhuber, and Harri Valpola · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, and Kilian Q Weinberger · 2016
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An alternative update rule for generative adversarial networks
Ferenc Huszár · 2016
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Pixel recurrent neural networks
Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
Wenzhe Shi, Jose Caballero, Ferenc Huszar, Johannes Totz, Andrew P Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang · 2016
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Theano: A python framework for fast computation of mathematical expressions
The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, et al · 2016
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Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou · 2017
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