1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 - PowerPoint PPT Presentation

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

• [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 36

• [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 37

• [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, “BM 3D 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 38