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[1] E. Luo, S. Pan and T. Nguyen, “Generalized non-local means for iterative denoising,” in Proc. 20th Euro. Signal Process. Conf. (EUSIPCO’12), pp. 260-264, Aug. 2012 [2] E. Luo, S.H. Chan, S. Pan and T.Q. Nguyen, “Adaptive non-local means for multiview image denoising: Searching for the right patches via a statistical approach,” in Proc. IEEE Intl. Conf. Image Process. (ICIP’13), pp. 543-547, Sep. 2013 [3] E. Luo, S.H. Chan and T.Q. Nguyen, “Image denoising by targeted external databases,” in Proc. IEEE Intl. Conf. Acoustics, Speech and Signal Process. (ICASSP’14), pp. 2469-2473, May 2014 [4] E. Luo, S.H. Chan and T.Q. Nguyen, “Adaptive image denoising by targeted databases,” submitted to IEEE Trans. Image Process.(TIP’14), 2014

[1] A. Buades, B. Coll and J. Morel, “A review of image denoising algorithms, with a new one,” SIAM Multi. Model. Simul, 2005 [2] K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, “Image denoising by sparse 3D transform-domain collaborative filtering,” IEEE Trans. Image Process.(TIP’07), 2007 [3] P. Chatterjee and P. Milanfar, “Is denoising dead?,” IEEE Trans. Image. Process.(TIP’10), 2010 [4] P. Chatterjee and P. Milanfar, “Practical bounds on image denoising: From estimation to information,” IEEE Trans. Image. Process.(TIP’11), 2011 [5] A. Levin and B. Nadler, “Natural image denoising: Optimality and inherent bounds,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition(CVPR’11), 2011 [6] A. Levin, B. Nadler, F. Durand and W. Freeman, “Patch complexity, finite pixel correlations and optimal denoising,” European Conference on Computer Vision(ECCV’12), 2012 [7] W. Freeman, T. Jone, and E. Pasztor, “Example-based super resolution,” in IEEE Journal on Computer Graphics and Applications(JCGA’02), 2002

[8] M. Elad and D. Datsenko, “Example-based regularization deployed to super-resolution reconstruction of a single image,” The Computer Journal(CJ’09), 2009 [9] L. Sun and J. Hays, “Super-resolution from internet-scale scene matching,” in Proc. IEEE Intl. Conf. Computational Photography(ICCP’12), 2012 [10] M. Aharon, M. Elad and A. Bruckstein, “K-SVD: Design of dictionaries for sparse representation,” in Proc. Signal Processing with Adaptive Sparse Structured Representations(SPARS’05), 2005 [11] J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, “Non-local sparse models for image restoration,” in IEEE Conf. Computer Vision and Pattern Recognition(CVPR’09), 2009 [12] D. Zoran and Y. Weiss, “From learning models of natural image patches to whole image restoration,” in Proc. IEEE Intl. Conf. Computer Vision(ICCV’11), 2011 [13] S.H. Chan, T. Zickler, and Y.M. Lu, “Fast non-local filtering by random sampling: it works, especially for large images,” in Proc. IEEE Intl. Conf. Acoustics, Speech and Signal Process. (ICASSP’13), 2013

[14] L. Zhang, W. Dong, D. Zhang, and G [14] L. Zhang, W. Dong, D. Zhang, and G. Shi, “Two-stage image denoising by principal component analysis with local pixel grouping,” Pattern Recognition(PR’10), 2010 [15] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “BM3D image denoising with shape-adaptive principal component analysis,” in Proc. Signal Processing with Adaptive Sparse Structured Representations(SPARS’09), 2009 [16] L. Zhang, S. Vaddadi, H. Jin, and S. Nayar, “Multiple view image denoising,” in Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR’09), 2009