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Recent works on adaptive sparse and on low-rank signal modeling have demonstrated their usefulness in various image / video processing applications.
L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D: nonlinear phenomena , vol. 60, no. 1-4, pp. 259–268, 1992
1992
Earlier work this paper cites.
Y. Pati, R. Rezaiifar, and P. Krishnaprasad, “Orthogonal matching pursuit : recursive function approximation with applications to wavelet decomposition,” in Asilomar Conf. on Signals, Systems and Computer , 1993, pp. 40–44 vol.1
1993
Earlier work this paper cites.
G. Davis, S. Mallat, and M. Avellaneda, “Adaptive greedy approximations,” Journal of Constructive Approximation , vol. 13, no. 1, pp. 57–98, 1997
1997
Earlier work this paper cites.
N. Weyrich and G. T. Warhola, “Wavelet shrinkage and generalized cross validation for image denoising,” IEEE Trans. Image Proc. , vol. 7, no. 1, pp. 82–90, 1998
1998
Earlier work this paper cites.
M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester, “Image inpainting,” in Annual Conference on Computer Graphics and Interactive Techniques . ACM Press/Addison-Wesley Publishing Co., 2000, pp. 417–424
2000
Earlier work this paper cites.
C. Ballester, M. Bertalmio, V. Caselles, G. Sapiro, and J. Verdera, “Filling-in by joint interpolation of vector fields and gray levels,” IEEE Trans. Image Proc. , vol. 10, no. 8, pp. 1200–1211, 2001
2001
Earlier work this paper cites.
Y.-S. Chen, Y.-P. Hung, and C.-S. Fuh, “Fast block matching algorithm based on the winner-update strategy,” IEEE Trans. Image Proc. , vol. 10, no. 8, pp. 1212–1222, 2001
2001
Earlier work this paper cites.
A. Levin, A. Zomet, and Y. Weiss, “Learning how to inpaint from global image statistics,” in null . IEEE, 2003, p. 305
2003
Earlier work this paper cites.
M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. on Signal Processing , vol. 54, no. 11, pp. 4311–4322, 2006
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.
O. G. Guleryuz, “Nonlinear approximation based image recovery using adaptive sparse reconstructions and iterated denoising-part ii: adaptive algorithms,” IEEE Trans. Image Proc. , vol. 15, no. 3, pp. 555–571, 2006
2006
Earlier work this paper cites.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Proc. , vol. 16, no. 8, pp. 2080–2095, Aug 2007
2007
Earlier work this paper cites.
M. Lustig, D. Donoho, and J. M. Pauly, “Sparse MRI: The application of compressed sensing for rapid mr imaging,” Magnetic resonance in medicine , vol. 58, no. 6, pp. 1182–1195, 2007
2007
Earlier work this paper cites.
E. Liberty, F. Woolfe, P.-G. Martinsson, V. Rokhlin, and M. Tygert, “Randomized algorithms for the low-rank approximation of matrices,” Proceedings of the National Academy of Sciences , vol. 104, no. 51, pp. 20 167–20 172, 2007
2007
Earlier work this paper cites.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Color image denoising via sparse 3d collaborative filtering with grouping constraint in luminance-chrominance space,” in IEEE International Conference on Image Processing (ICIP) , vol. 1. IEEE, 2007, pp. I–313
2007
Earlier work this paper cites.
H. Takeda, S. Farsiu, and P. Milanfar, “Kernel regression for image processing and reconstruction,” IEEE Trans. Image Proc. , vol. 16, no. 2, pp. 349–366, 2007
2007
Earlier work this paper cites.
A. Buades, B. Coll, and J.-M. Morel, “Nonlocal image and movie denoising,” Int. Journal of Computer Vision , vol. 76, no. 2, pp. 123–139, 2008
2008
Earlier work this paper cites.
M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, “Compressed sensing MRI,” IEEE signal processing magazine , vol. 25, no. 2, pp. 72–82, 2008
2008
Earlier work this paper cites.
B. Kaltenbacher, A. Neubauer, and O. Scherzer, Iterative regularization methods for nonlinear ill-posed problems . Walter de Gruyter, 2008, vol. 6
2008
Earlier work this paper cites.
J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, “Non-local sparse models for image restoration,” in IEEE Int. Conf. Computer Vision (ICCV 2009) , Sept 2009, pp. 2272–2279
2009
Earlier work this paper cites.
A. Beck and M. Teboulle, “Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems,” IEEE Trans. Image Proc. , vol. 18, no. 11, pp. 2419–2434, 2009
2009
Earlier work this paper cites.
S. Roth and M. J. Black, “Fields of experts,” Int. Journal of Computer Vision , vol. 82, no. 2, p. 205, 2009
2009
Earlier work this paper cites.
J. Trzasko and A. Manduca, “Highly undersampled magnetic resonance image reconstruction via homotopic ℓ 0 \ell_{0} -minimization,” IEEE Transactions on Medical imaging , vol. 28, no. 1, pp. 106–121, 2009
2009
Earlier work this paper cites.
M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing . New York:Springer, 2010
2010
Earlier work this paper cites.
Z. Xu and J. Sun, “Image inpainting by patch propagation using patch sparsity,” IEEE Trans. Image Proc. , vol. 19, no. 5, pp. 1153–1165, 2010
2010
Earlier work this paper cites.
H. Ji, C. Liu, Z. Shen, and Y. Xu, “Robust video denoising using low rank matrix completion,” in IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2010) , June 2010, pp. 1791–1798
2010
Earlier work this paper cites.
G. Yu and G. Sapiro, “DCT image denoising: a simple and effective image denoising algorithm,” Image Proc. OL , vol. 1, pp. 292–296, 2011
2011
Earlier work this paper cites.
W. Dong, X. Li, L. Zhang, and G. Shi, “Sparsity-based image denoising via dictionary learning and structural clustering,” in IEEE Conf. Computer Vision and Pattern Recognition (CVPR) , June 2011, pp. 457–464
2011
Cited alongside, same era.
M. Zontak and M. Irani, “Internal statistics of a single natural image,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2011, pp. 977–984
2011
Cited alongside, same era.
D. Zoran and Y. Weiss, “From learning models of natural image patches to whole image restoration,” in IEEE International Conference on Computer Vision (ICCV) . IEEE, 2011, pp. 479–486
2011
Cited alongside, same era.
X. Li, “Image recovery via hybrid sparse representations: A deterministic annealing approach,” IEEE Journal of Selected Topics in Signal Processing , vol. 5, no. 5, pp. 953–962, 2011
2011
Cited alongside, same era.
S. Ravishankar and Y. Bresler, “Data-driven learning of a union of sparsifying transforms model for blind compressed sensing,” IEEE Transactions on Computational Imaging , vol. 2, no. 3, pp. 294 – 309, 2015
2015
Later among the works it cites.
S. Ravishankar and Y. Bresler, “Efficient blind compressed sensing using sparsifying transforms with convergence guarantees and application to magnetic resonance imaging,” SIAM Journal on Imaging Sciences , vol. 8, no. 4, pp. 2519–2557, 2015
2015
Later among the works it cites.
F. Chen, L. Zhang, and H. Yu, “External patch prior guided internal clustering for image denoising,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 603–611
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 Proc. , vol. 24, no. 11, pp. 4359–4371, 2015
2015
Later among the works it cites.
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S. Ravishankar and Y. Bresler, “MR image reconstruction from highly undersampled k-space data by dictionary learning,” IEEE Transactions on Medical Imaging , vol. 30, no. 5, pp. 1028–1041, 2011
2011
Cited alongside, same era.
S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Eckstein et al. , “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine Learning , vol. 3, no. 1, pp. 1–122, 2011
2011
Cited alongside, same era.
T. Zhou and D. Tao, “Godec: Randomized low-rank & sparse matrix decomposition in noisy case,” in International Conference on Machine Learning . Omnipress, 2011
2011
Cited alongside, same era.
H. C. Burger, C. J. Schuler, and S. Harmeling, “Image denoising: Can plain neural networks compete with bm3d?” in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on . IEEE, 2012, pp. 2392–2399
2012
Cited alongside, same era.
R. Rubinstein, T. Peleg, and M. Elad, “Analysis K-SVD: A dictionary-learning algorithm for the analysis sparse model,” IEEE Trans. Sig. Proc. , vol. 61, no. 3, pp. 661–677, 2013
2013
Cited alongside, same era.
W. Dong, G. Shi, and X. Li, “Nonlocal image restoration with bilateral variance estimation: A low-rank approach,” IEEE Trans. Image Proc. , vol. 22, no. 2, pp. 700–711, 2013
2013
Cited alongside, same era.
H. C. Burger, C. Schuler, and S. Harmeling, “Learning how to combine internal and external denoising methods,” in German Conference on Pattern Recognition . Springer, 2013, pp. 121–130
2013
Cited alongside, same era.
I. Ram, M. Elad, and I. Cohen, “Image processing using smooth ordering of its patches,” IEEE Trans. Image Proc. , vol. 22, no. 7, pp. 2764–2774, 2013
2013
Cited alongside, same era.
W. Dong, G. Shi, Y. Ma, and X. Li, “Image restoration via simultaneous sparse coding: Where structured sparsity meets gaussian scale mixture,” Int. Journal of Computer Vision , vol. 114, no. 2-3, pp. 217–232, 2015
2015
Later among the works it cites.
J. Li, X. Chen, D. Zou, B. Gao, and W. Teng, “Conformal and low-rank sparse representation for image restoration,” in IEEE Int. Conf. Computer Vision (ICCV) , Dec 2015, pp. 235–243
2015
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J. Xu, L. Zhang, W. Zuo, D. Zhang, and X. Feng, “Patch group based nonlocal self-similarity prior learning for image denoising,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 244–252
2015
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K. H. Jin and J. C. Ye, “Annihilating filter-based low-rank hankel matrix approach for image inpainting,” IEEE Trans. Image Proc. , vol. 24, no. 11, pp. 3498–3511, 2015
2015
Later among the works it cites.
O. N. Jaspan, R. Fleysher, and M. L. Lipton, “Compressed sensing MRI: a review of the clinical literature,” The British journal of radiology , vol. 88, no. 1056, p. 20150487, 2015
2015
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Y. Romano and M. Elad, “Boosting of image denoising algorithms,” SIAM Journal on Imaging Sciences , vol. 8, no. 2, pp. 1187–1219, 2015
2015
Later among the works it cites.
B. Wen, S. Ravishankar, and Y. Bresler, “Learning flipping and rotation invariant sparsifying transforms,” in IEEE International Conference on Image Processing (ICIP) , 2016, pp. 3857–3861
2016
Later among the works it cites.
Y. Yang, J. Sun, H. Li, and Z. Xu, “Deep admm-net for compressive sensing MRI,” in Advances in Neural Information Processing Systems , 2016, pp. 10–18
2016
Later among the works it cites.
B. Wen, Y. Li, and Y. Bresler, “When sparsity meets low-rankness: Transform learning with non-local low-rank constraint for image restoration,” in IEEE Int. Conf. Acoustics, Speech and Siginal Proc. IEEE, 2017, pp. 2297–2301
2017
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S. Gu, Q. Xie, D. Meng, W. Zuo, X. Feng, and L. Zhang, “Weighted nuclear norm minimization and its applications to low level vision,” Int. Journal of Computer Vision , vol. 121, no. 2, pp. 183–208, 2017
2017
Later among the works it cites.
Z. Zha, X. Liu, Z. Zhou, X. Huang, J. Shi, Z. Shang, L. Tang, Y. Bai, Q. Wang, and X. Zhang, “Image denoising via group sparsity residual constraint,” in IEEE Int. Conf. Acoustics, Speech and Siginal Proc. IEEE, 2017, pp. 1787–1791
2017
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B. Wen, Y. Li, L. Pfister, and Y. Bresler, “Joint adaptive sparsity and low-rankness on the fly: an online tensor reconstruction scheme for video denoising,” in IEEE International Conference on Computer Vision (ICCV) , 2017, pp. 241–250
2017
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2017
Later among the works it cites.
R. Yin, T. Gao, Y. M. Lu, and I. Daubechies, “A tale of two bases: Local-nonlocal regularization on image patches with convolution framelets,” SIAM Journal on Imaging Sciences , vol. 10, no. 2, pp. 711–750, 2017
2017
Later among the works it cites.
S. Osher, Z. Shi, and W. Zhu, “Low dimensional manifold model for image processing,” SIAM Journal on Imaging Sciences , vol. 10, no. 4, pp. 1669–1690, 2017
2017
Later among the works it cites.
Y. Chen and T. Pock, “Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration,” IEEE Trans. Pattern Analysis and Machine Intel. , vol. 39, no. 6, pp. 1256–1272, 2017
2017
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K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising,” IEEE Trans. Image Proc. , vol. 26, no. 7, pp. 3142–3155, 2017
2017
Later among the works it cites.
B. Wen, S. Ravishankar, and Y. Bresler, “FRIST- flipping and rotation invariant sparsifying transform learning and applications,” Inverse Problems , vol. 33, no. 7, p. 074007, 2017
2017
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J. Xu, L. Zhang, D. Zhang, and X. Feng, “Multi-channel weighted nuclear norm minimization for real color image denoising,” in IEEE International Conference on Computer Vision (ICCV) , 2017
2017
Later among the works it cites.
W. Bae, J. Yoo, and J. C. Ye, “Beyond deep residual learning for image restoration: Persistent homology-guided manifold simplification,” in IEEE Conf. Comp. Vision and Pattern Recog. (CVPR) Workshops , 2017, pp. 145–153
2017
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2018
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J. C. Ye, Y. Han, and E. Cha, “Deep convolutional framelets: A general deep learning framework for inverse problems,” SIAM Journal on Imaging Sciences , vol. 11, no. 2, pp. 991–1048, 2018
2018
Closest in time.