Fetching the paper…
Reading the bibliography…
Image restoration, including image denoising, super resolution, inpainting, and so on, is a well-studied problem in computer vision and image processing, as well as a test bed for low-level image modeling algorithms.
L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D , vol. 60, no. 1-4, pp. 259–268, November 1992
1992
Earlier work this paper cites.
K. Sivakumar and U. B. Desai, “Image restoration using a multilayer perceptron with a multilevel sigmoidal function,” IEEE Trans. Signal Processing , vol. 41, no. 5, pp. 2018–2022, 1993
1993
Earlier work this paper cites.
D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in Proc. IEEE Int. Conf. Comp. Vis. , vol. 2, July 2001, pp. 416–423
2001
Earlier work this paper cites.
T. Chan, S. Esedoglu, F. Park, and A. Yip, “Recent developments in total variation image restoration,” in In Mathematical Models of Computer Vision . Springer Verlag, 2005
2005
Earlier work this paper cites.
Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, “Greedy layer-wise training of deep networks,” in Proc. Advances in Neural Inf. Process. Syst. , 2006, pp. 153–160
2006
Earlier work this paper cites.
M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. , vol. 15, no. 12, pp. 3736–3745, 2006
2006
Earlier work this paper cites.
A. Foi, V. Katkovnik, and K. O. Egiazarian, “Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images,” IEEE Trans. Image Process. , vol. 16, no. 5, pp. 1395–1411, 2007
2007
Earlier work this paper cites.
K. Dabov, A. Foi, V. Katkovnik, and K. O. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Trans. Image Processing , vol. 16, no. 8, pp. 2080–2095, 2007
2007
Earlier work this paper cites.
A. Levin, R. Fergus, F. Durand, and W. T. Freeman, “Image and depth from a conventional camera with a coded aperture,” ACM Trans. Graph. , vol. 26, no. 3, p. 70, 2007
2007
Earlier work this paper cites.
J. Mairal, M. Elad, and G. Sapiro, “Sparse representation for color image restoration,” IEEE Trans. Image Process. , vol. 17, no. 1, pp. 53–69, 2008
2008
Earlier work this paper cites.
P. Vincent, H. Larochelle, Y. Bengio, and P. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in Proc. Int. Conf. Mach. Learn. , 2008, pp. 1096–1103
2008
Earlier work this paper cites.
V. Jain and H. S. Seung, “Natural image denoising with convolutional networks,” in Proc. Advances in Neural Inf. Process. Syst. , 2008, pp. 769–776
2008
Earlier work this paper cites.
J. Mairal, F. R. Bach, J. Ponce, G. Sapiro, and A. Zisserman, “Non-local sparse models for image restoration,” in Proc. IEEE Int. Conf. Comp. Vis. , 2009, pp. 2272–2279
2009
Earlier work this paper cites.
S. Roth and M. J. Black, “Fields of experts,” Int. J. Comput. Vision , vol. 82, no. 2, pp. 205–229, 2009
2009
Earlier work this paper cites.
P. Chatterjee and P. Milanfar, “Clustering-based denoising with locally learned dictionaries,” IEEE Trans. Image Process. , vol. 18, no. 7, pp. 1438–1451, 2009
2009
Earlier work this paper cites.
J. Oliveira, J. Bioucas-Dias, and M. A. T. Figueiredo, “Adaptive total variation image deblurring: a majorization-minimization approach,” Signal Processing , vol. 89, no. 9, pp. 2479–2493, September 2009
2009
Earlier work this paper cites.
D. Krishnan and R. Fergus, “Fast image deconvolution using hyper-laplacian priors,” in Proc. Advances in Neural Inf. Process. Syst. , 2009, pp. 1033–1041
2009
Earlier work this paper cites.
K. I. Kim and Y. Kwon, “Single-image super-resolution using sparse regression and natural image prior,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 32, no. 6, pp. 1127–1133, 2010
2010
Earlier work this paper cites.
J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process. , vol. 19, no. 11, pp. 2861–2873, 2010
2010
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proc. Int. Conf. Mach. Learn. , 2010, pp. 807–814
2010
Earlier work this paper cites.
D. Zoran and Y. Weiss, “From learning models of natural image patches to whole image restoration,” in Proc. IEEE Int. Conf. Comp. Vis. , 2011, pp. 479–486
2011
Earlier work this paper cites.
W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process. , vol. 20, no. 7, pp. 1838–1857, 2011
2011
Cited alongside, same era.
S. Cho, J. Wang, and S. Lee, “Handling outliers in non-blind image deconvolution,” in Proc. IEEE Int. Conf. Comp. Vis. , 2011, pp. 495–502
2011
Cited alongside, same era.
J. Xie, L. Xu, and E. Chen, “Image denoising and inpainting with deep neural networks,” in Proc. Advances in Neural Inf. Process. Syst. , 2012, pp. 350–358
2012
Cited alongside, same era.
J. Jancsary, S. Nowozin, and C. Rother, “Loss-specific training of non-parametric image restoration models: A new state of the art,” in Proc. Eur. Conf. Comp. Vis. , 2012, pp. 112–125
2012
Cited alongside, same era.
Y. Zhu, Y. Zhang, B. Bonev, and A. L. Yuille, “Modeling deformable gradient compositions for single-image super-resolution,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2015, pp. 5417–5425
2015
Later among the works it cites.
G. Riegler, S. Schulter, M. Rüther, and H. Bischof, “Conditioned regression models for non-blind single image super-resolution,” in Proc. IEEE Int. Conf. Comp. Vis. , 2015, pp. 522–530
2015
Later among the works it cites.
S. Gu, W. Zuo, Q. Xie, D. Meng, X. Feng, and L. Zhang, “Convolutional sparse coding for image super-resolution,” in Proc. IEEE Int. Conf. Comp. Vis. , 2015, pp. 1823–1831
2015
Later among the works it cites.
Z. Wang, D. Liu, J. Yang, W. Han, and T. S. Huang, “Deep networks for image super-resolution with sparse prior,” in Proc. IEEE Int. Conf. Comp. Vis. , 2015, pp. 370–378
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…
2012
Cited alongside, same era.
W. Dong, L. Zhang, G. Shi, and X. Li, “Nonlocally centralized sparse representation for image restoration,” IEEE Trans. Image Process. , vol. 22, no. 4, pp. 1620–1630, 2013
2013
Cited alongside, same era.
J. Yang, Z. Lin, and S. Cohen, “Fast image super-resolution based on in-place example regression,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2013, pp. 1059–1066
2013
Cited alongside, same era.
P. Milanfar, “A tour of modern image filtering: New insights and methods, both practical and theoretical,” IEEE Signal Process. Mag. , vol. 30, no. 1, pp. 106–128, 2013
2013
Cited alongside, same era.
C. J. Schuler, H. C. Burger, S. Harmeling, and B. Schölkopf, “A machine learning approach for non-blind image deconvolution,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2013, pp. 1067–1074
2013
Cited alongside, same era.
U. Schmidt and S. Roth, “Shrinkage fields for effective image restoration,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2014, pp. 2774–2781
2014
Cited alongside, same era.
S. Gu, L. Zhang, W. Zuo, and X. Feng, “Weighted nuclear norm minimization with application to image denoising,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2014, pp. 2862–2869
2014
Cited alongside, same era.
R. Timofte, V. D. Smet, and L. J. V. Gool, “A+: adjusted anchored neighborhood regression for fast super-resolution,” in Proc. Asian Conf. Comp. Vis. , 2014, pp. 111–126
2014
Cited alongside, same era.
C. Dong, Y. Deng, C. C. Loy, and X. Tang, “Compression artifacts reduction by a deep convolutional network,” in Proc. IEEE Int. Conf. Comp. Vis. , 2015, pp. 576–584
2015
Later among the works it cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2015, pp. 3431–3440
2015
Later among the works it cites.
Z. Wang, Y. Yang, Z. Wang, S. Chang, J. Yang, and T. S. Huang, “Learning super-resolution jointly from external and internal examples,” IEEE Trans. Image Process. , vol. 24, no. 11, pp. 4359–4371, 2015
2015
Later among the works it cites.
H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation,” in Proc. IEEE Int. Conf. Comp. Vis. , 2015, pp. 1520–1528
2015
Later among the works it cites.
S. Hong, H. Noh, and B. Han, “Decoupled deep neural network for semi-supervised semantic segmentation,” in Proc. Advances in Neural Inf. Process. Syst. , 2015
2015
Later among the works it cites.
R. K. Srivastava, K. Greff, and J. Schmidhuber, “Training very deep networks,” in Proc. Advances in Neural Inf. Process. Syst. , 2015
2015
Later among the works it cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. Int. Conf. Learning Representations , 2015
2015
Later among the works it cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” Proc. Int. Conf. Learn. Representations , 2015
2015
Later among the works it cites.
J. Salvador and E. Perez-Pellitero, “Naive bayes super-resolution forest,” in Proc. IEEE Int. Conf. Comp. Vis. , 2015, pp. 325–333
2015
Later among the works it cites.
J. Huang, A. Singh, and N. Ahuja, “Single image super-resolution from transformed self-exemplars,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2015, pp. 5197–5206
2015
Later among the works it cites.
S. Schulter, C. Leistner, and H. Bischof, “Fast and accurate image upscaling with super-resolution forests,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2015, pp. 3791–3799
2015
Later among the works it cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2016
2016
Closest in time.
U. Schmidt, J. Jancsary, S. Nowozin, S. Roth, and C. Rother, “Cascades of regression tree fields for image restoration,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 38, no. 4, pp. 677–689, 2016
2016
Closest in time.
C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 38, no. 2, pp. 295–307, 2016
2016
Closest in time.
J. Kim, J. K. Lee, and K. M. Lee, “Accurate image super-resolution using very deep convolutional networks,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2016
2016
Closest in time.
——, “Deeply-recursive convolutional network for image super-resolution,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2016
2016
Closest in time.
X. Mao, C. Shen, and Y. Yang, “Image denoising using very deep fully convolutional encoder-decoder networks with symmetric skip connections,” in Proc. Advances in Neural Inf. Process. Syst. , 2016
2016
Closest in time.