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We propose an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN).
Improving resolution by image registration
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Learning long-term dependencies with gradient descent is difficult
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Gradient-based learning applied to document recognition
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Super-resolution through neighbor embedding
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Image super-resolution using gradient profile prior
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Super-resolution from a single image
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Single-image super-resolution using sparse regression and natural image prior
K. I. Kim and Y. Kwon · 2010
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Image super-resolution via sparse representation
J. Yang, J. Wright, T. S. Huang, and Y. Ma · 2010
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Low-complexity single-image super-resolution based on nonnegative neighbor embedding
C. G. Marco Bevilacqua, Aline Roumy and M.-L. A. Morel · 2012
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R. Socher, B. Huval, B. Bath, C. D. Manning, and A. Y. Ng · 2012
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Semantic compositionality through recursive matrix-vector spaces
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On single image scale-up using sparse-representations
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Super-resolution using neighbor embedding of back-projection residuals
M. Bevilacqua, A. Roumy, C. Guillemot, and M.-L. Morel · 2013
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2014
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Recurrent convolutional neural networks for scene labeling
P. Pinheiro and R. Collobert · 2014
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A+: Adjusted anchored neighborhood regression for fast super-resolution
R. Timofte, V. De Smet, and L. Van Gool · 2014
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Matconvnet – convolutional neural networks for matlab
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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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Anchored neighborhood regression for fast example-based super-resolution
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Image super-resolution using deep convolutional networks
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Understanding deep architectures using a recursive convolutional network
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