Fetching the paper…
Reading the bibliography…
In this paper, we propose a very deep fully convolutional encoding-decoding framework for image restoration such as denoising and super-resolution.
Nonlinear total variation based noise removal algorithms
L. I. Rudin, S. Osher, and E. Fatemi · 1992
Earlier work this paper cites.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
D. Martin, C. Fowlkes, D. Tal, and J. Malik · 2001
Earlier work this paper cites.
An iterative regularization method for total variation-based image restoration
S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin · 2005
Earlier work this paper cites.
Image denoising by sparse 3-d transform-domain collaborative filtering
K. Dabov, A. Foi, V. Katkovnik, and K. O. Egiazarian · 2007
Earlier work this paper cites.
Natural image denoising with convolutional networks
V. Jain and H. S. Seung · 2008
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders
P. Vincent, H. Larochelle, Y. Bengio, and P. Manzagol · 2008
Earlier work this paper cites.
Clustering-based denoising with locally learned dictionaries
P. Chatterjee and P. Milanfar · 2009
Earlier work this paper cites.
From learning models of natural image patches to whole image restoration
D. Zoran and Y. Weiss · 2011
Earlier work this paper cites.
Image denoising: Can plain neural networks compete with BM3D?
H. C. Burger, C. J. Schuler, and S. Harmeling · 2012
Earlier work this paper cites.
Image denoising and inpainting with deep neural networks
J. Xie, L. Xu, and E. Chen · 2012
Earlier work this paper cites.
Nonlocally centralized sparse representation for image restoration
W. Dong, L. Zhang, G. Shi, and X. Li · 2013
Earlier work this paper cites.
A tour of modern image filtering: New insights and methods, both practical and theoretical
P. Milanfar · 2013
Earlier work this paper cites.
Deep network cascade for image super-resolution
Z. Cui, H. Chang, S. Shan, B. Zhong, and X. Chen · 2014
Cited alongside, same era.
Weighted nuclear norm minimization with application to image denoising
S. Gu, L. Zhang, W. Zuo, and X. Feng · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Cited alongside, same era.
External patch prior guided internal clustering for image denoising
F. Chen, L. Zhang, and H. Yu · 2015
Cited alongside, same era.
Compression artifacts reduction by a deep convolutional network
C. Dong, Y. Deng, C. C. Loy, and X. Tang · 2015
Cited alongside, same era.
Convolutional sparse coding for image super-resolution
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Later among the works it cites.
Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
Later among the works it cites.
Naive bayes super-resolution forest
J. Salvador and E. Perez-Pellitero · 2015
Later among the works it cites.
Fast and accurate image upscaling with super-resolution forests
S. Schulter, C. Leistner, and H. Bischof · 2015
Later among the works it cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Later among the works it cites.
Training very deep networks
R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Gu, W. Zuo, Q. Xie, D. Meng, X. Feng, and L. Zhang · 2015
Cited alongside, same era.
Decoupled deep neural network for semi-supervised semantic segmentation
S. Hong, H. Noh, and B. Han · 2015
Cited alongside, same era.
Single image super-resolution from transformed self-exemplars
J. Huang, A. Singh, and N. Ahuja · 2015
Cited alongside, same era.
Bidirectional recurrent convolutional networks for multi-frame super-resolution
Y. Huang, W. Wang, and L. Wang · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Cited alongside, same era.
Image denoising via adaptive soft-thresholding based on non-local samples
H. Liu, R. Xiong, J. Zhang, and W. Gao · 2015
Cited alongside, same era.
Deep networks for image super-resolution with sparse prior
Z. Wang, D. Liu, J. Yang, W. Han, and T. S. Huang · 2015
Later among the works it cites.
Learning super-resolution jointly from external and internal examples
Z. Wang, Y. Yang, Z. Wang, S. Chang, J. Yang, and T. S. Huang · 2015
Later among the works it cites.
Patch group based nonlocal self-similarity prior learning for image denoising
J. Xu, L. Zhang, W. Zuo, D. Zhang, and X. Feng · 2015
Later among the works it cites.
Image super-resolution using deep convolutional networks
C. Dong, C. C. Loy, K. He, and X. Tang · 2016
Closest in time.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Closest in time.