American Institute of Mathematical Sciences

Advanced
August  2018, 12(4): 853-881. doi: 10.3934/ipi.2018036

Convergence theorems for the Non-Local Means filter

Qiyu Jin 1, , Ion Grama 2, and  Quansheng Liu 2,3,,

1. 

School of mathematical science, Inner Mongolia University, No.235 Daxuexilu Road, 010021 Hohhot, Inner Mongolia, China
2. 

UMR 6205, Laboratoire de Mathématiques de Bretagne Atlantique, Université de Bretagne-Sud, Campus de Tohannic, BP 573, 56017 Vannes, France
3. 

School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China

* Corresponding author: quansheng.liu@univ-ubs.fr

Received  March 2017 Revised  December 2017 Published  June 2018

We introduce an oracle filter for removing the Gaussian noise with weights depending on a similarity function. The usual Non-Local Means filter is obtained from this oracle filter by substituting the similarity function by an estimator based on similarity patches. When the sizes of the search window are chosen appropriately, it is shown that the oracle filter converges with the optimal rate. The same optimal convergence rate is preserved when the similarity function has suitable errors-in measurements. We also provide a statistical estimator of the similarity which converges at a convenient rate. Based on our convergence theorems, we propose some simple formulas for the choice of the parameters. Simulation results show that our choice of parameters improves the restoration quality of the filter compared with the usual choice of parameters in the original algorithm.

Keywords: Image denoising, oracle filter, Non-Local Means filter, weighted means, convergence, mean squared error.
Mathematics Subject Classification: 62H35.
Citation: Qiyu Jin, Ion Grama, Quansheng Liu. Convergence theorems for the Non-Local Means filter. Inverse Problems & Imaging, 2018, 12 (4) : 853-881. doi: 10.3934/ipi.2018036
References:
[1]

M. Aharon, M. Elad and A. Bruckstein, rmk-svd: An algorithm for designing overcomplete dictionaries for sparse representation, IEEE Trans. Signal Process., 54 (2006), 4311-4322.  doi: 10.1109/TSP.2006.881199.  Google Scholar

[2]

E. Arias-Castro, J. Salmon and R. Willett, Oracle inequalities and minimax rates for non-local means and related adaptive kernel-based methods, Siam Journal on Imaging Sciences, 5 (2012), 944-992.  doi: 10.1137/110859403.  Google Scholar

[3]

R. C. Bilcu and M. Vehvilainen, Fast nonlocal means for image denoising, In Proc. of SPIE Conf. on Digital Photography III, 6502 (2007), 65020R. doi: 10.1117/12.695079.  Google Scholar

[4]

J. Boulanger, C. Kervrann, P. Bouthemy, P. Elbau, J. B. Sibarita and J. Salamero, Patch-based nonlocal functional for denoising fluorescence microscopy image sequences, IEEE Transactions on Medical Imaging, 29 (2010), 442-454.  doi: 10.1109/TMI.2009.2033991.  Google Scholar

[5]

A. Buades, B. Coll and J. M. Morel, A review of image denoising algorithms, with a new one, Multiscale Model. Simul., 4 (2005), 490-530.  doi: 10.1137/040616024.  Google Scholar

[6]

A. Buades, B. Coll and J. M. Morel, The staircasing effect in neighborhood filters and its solution, IEEE Trans. Image Process., 15 (2006), 1499-1505.  doi: 10.1109/TIP.2006.871137.  Google Scholar

[7]

T. Buades, Y. Lou, J. M. Morel and Z. Tang, A note on multi-image denoising, In Int. workshop on Local and Non-Local Approximation in Image Processing, pages 1–15, August 2009. doi: 10.1109/LNLA.2009.5278408.  Google Scholar

[8]

P. Chatterjee and P. Milanfar, A generalization of non-local means via kernel regression, In Proc. of SPIE Conf. on Computational Imaging, Citeseer, 6814 (2008), 6814Op. doi: 10.1117/12.778615.  Google Scholar

[9]

K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Trans. Image Process., 16 (2007), 2080-2095.  doi: 10.1109/TIP.2007.901238.  Google Scholar

[10]

K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, Bm3d image denoising with shape-adaptive principal component analysis, In Proc. Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS 09), volume 49, Citeseer, 2009. Google Scholar

[11]

C. A. Deledalle, V. Duval and J. Salmon, Non-local methods with shape-adaptive patches (nlm-sap), Journal of Mathematical Imaging and Vision, 43 (2012), 103-120.  doi: 10.1007/s10851-011-0294-y.  Google Scholar

[12]

D. L. Donoho and J. M. Johnstone, Ideal spatial adaptation by wavelet shrinkage, Biometrika, 81 (1994), 425-455.  doi: 10.1093/biomet/81.3.425.  Google Scholar

[13]

V. Duval, J.-F. Aujol and Y. Gousseau, A bias-variance approach for the nonlocal means, SIAM Journal on Imaging Sciences, 4 (2011), 760-788.  doi: 10.1137/100790902.  Google Scholar

[14]

J. Q. Fan and I. Gijbels, Local Polynomial Modelling and Its Applications, Chapman & Hall, London, 1996.  Google Scholar

[15]

A. Foi, V. Katkovnik, K. Egiazarian and J. Astola, A novel anisotropic local polynomial estimator based on directional multiscale optimizations, In Proc. 6th IMA Int. Conf. Math. in Signal Process, pages 79–82, Citeseer. Google Scholar

[16]

D. K. Hammond and E. P. Simoncelli, Image modeling and denoising with orientation-adapted gaussian scale mixtures, IEEE Trans. Image Process., 17 (2008), 2089-2101.  doi: 10.1109/TIP.2008.2004796.  Google Scholar

[17]

K. Hirakawa and T. W. Parks, Image denoising using total least squares, IEEE Trans. Image Process., 15 (2006), 2730-2742.  doi: 10.1109/TIP.2006.877352.  Google Scholar

[18]

H. Hu, B. Li and Q. Liu, Removing mixture of gaussian and impulse noise by patch-basedweighted means, Journal of Scientific Computing, 67 (2016), 103-129.  doi: 10.1007/s10915-015-0073-9.  Google Scholar

[19]

J. Immerkaer, Fast noise variance estimation, Computer vision and image understanding, 64 (1996), 300-302.  doi: 10.1006/cviu.1996.0060.  Google Scholar

[20]

Q. Jin, I. Grama, C. Kervrann and Q. Liu, Nonlocal means and optimal weights for noise removal, SIAM Journal on Imaging Sciences, 10 (2017), 1878-1920.  doi: 10.1137/16M1080781.  Google Scholar

[21]

Q. Jin, I. Grama and Q. Liu, Removing gaussian noise by optimization of weights in non-local means, preprint, arXiv: 1109.5640. Google Scholar

[22]

Q. Jin, I. Grama and Q. Liu, A new poisson noise filter based on weights optimization, Journal of Scientific Computing, 58 (2014), 548-573.  doi: 10.1007/s10915-013-9743-7.  Google Scholar

[23]

V. Karnati, M. Uliyar and S. Dey, Fast non-local algorithm for image denoising, In IEEE International Conference on Image Processing (ICIP), 2009 16th, pages 3873–3876, IEEE, 2009. doi: 10.1109/ICIP.2009.5414044.  Google Scholar

[24]

V. Katkovnik, A. Foi, K. Egiazarian and J. Astola, Directional varying scale approximations for anisotropic signal processing, In Proc. XII European Signal Proc. Conf., EUSIPCO 2004, Vienna, pages 101–104, 2004. Google Scholar

[25]

V. Katkovnik, A. Foi, K. Egiazarian and J. Astola, From local kernel to nonlocal multiple-model image denoising, Int. J. Comput. Vis., 86 (2010), 1-32.  doi: 10.1007/s11263-009-0272-7.  Google Scholar

[26]

C. Kervrann and J. Boulanger, Optimal spatial adaptation for patch-based image denoising, IEEE Trans. Image Process., 15 (2006), 2866-2878.  doi: 10.1109/TIP.2006.877529.  Google Scholar

[27]

C. Kervrann and J. Boulanger, Local adaptivity to variable smoothness for exemplar-based image regularization and representation, Int. J. Comput. Vis., 79 (2008), 45-69.  doi: 10.1007/s11263-007-0096-2.  Google Scholar

[28]

M. Lebrun, A. Buades and J. M. Morel, Implementation of the "Non-Local Bayes" (NL-bayes) image denoising algorithm, Image Processing On Line, 2013 (2013), 1-42.   Google Scholar

[29]

M. Lebrun, A. Buades and J. M. Morel, A nonlocal bayesian image denoising algorithm, SIAM Journal on Imaging Sciences, 6 (2013), 1665-1688.  doi: 10.1137/120874989.  Google Scholar

[30]

A. Levin and B. Nadler, Natural image denoising: Optimality and inherent bounds, In Computer Vision and Pattern Recognition, pages 2833–2840, 2011. doi: 10.1109/CVPR.2011.5995309.  Google Scholar

[31]

B. Li, Q. Liu, J. Xu and X. Luo, A new method for removing mixed noises, Science China Information Sciences, 54 (2011), 51-59.  doi: 10.1007/s11432-010-4128-0.  Google Scholar

[32]

Y. Lou, X. Zhang, S. Osher and A. Bertozzi, Image recovery via nonlocal operators, J. Sci. Comput., 42 (2010), 185-197.  doi: 10.1007/s10915-009-9320-2.  Google Scholar

[33]

M. Mahmoudi and G. Sapiro, Fast image and video denoising via nonlocal means of similar neighborhoods, IEEE Signal. Proc. Let., 12 (2005), 839-842.  doi: 10.1109/LSP.2005.859509.  Google Scholar

[34]

A. Maleki, M. Narayan and R. Baraniuk, Suboptimality of Nonlocal Means on Images with Sharp Edges, In Annual Allerton Conference on Communication, Control, and Computing, 2011. Google Scholar

[35]

A. Maleki, M. Narayan and R. Baraniuk, Anisotropic nonlocal means denoising, Applied and Computational Harmonic Analysis, 35 (2013), 452-482.  doi: 10.1016/j.acha.2012.11.003.  Google Scholar

[36]

J. Polzehl and V. Spokoiny, Propagation-separation approach for local likelihood estimation, Probab. Theory Rel. Fields, 135 (2006), 335-362.  doi: 10.1007/s00440-005-0464-1.  Google Scholar

[37]

S. Roth and M. J. Black, Fields of experts, Int. J. Comput. Vision, 82 (2009), 205-229.  doi: 10.1007/s11263-008-0197-6.  Google Scholar

[38]

J. Salmon and E. Le Pennec, Nl-means and aggregation procedures, In IEEE Int. Conf. Image Process. (ICIP), pages 2977–2980. IEEE, 2009. doi: 10.1109/ICIP.2009.5414512.  Google Scholar

[39]

N. A. Thacker, P. A. Bromiley and J. V. Manjonb, A quantitative theory of the non-local means algorithm, In Proc. MIUA 2008, Dundee, Scotland, pages 174–178. Citeseer, 2008. Google Scholar

[40]

C. Tomasi and R. Manduchi, Bilateral filtering for gray and color images, In Proc. Int. Conf. Computer Vision, pages 839–846, 1998. doi: 10.1109/ICCV.1998.710815.  Google Scholar

[41]

D. Van De Ville and M. Kocher, Non-local means with dimensionality reduction and sure-based parameter selection, IEEE Trans. Image Process., 20 (2010), 2683-2690.  doi: 10.1109/TIP.2011.2121083.  Google Scholar

[42]

R. Vignesh, B. T. Oh and C. C. J. Kuo, Fast non-local means (nlm) computation with probabilistic early termination, IEEE Signal. Proc. Let., 17 (2010), 277-280.  doi: 10.1109/LSP.2009.2038956.  Google Scholar

[43]

Y. Q. Wang, The implementation of sure guided piecewise linear image denoising, Image Processing On Line, 2013 (2013), 43-67.  doi: 10.5201/ipol.2013.52.  Google Scholar

[44]

L. P. Yaroslavsky, Digital Picture Processing. An Introduction, In Springer-Verlag, Berlin, 1985. doi: 10.1007/978-3-642-81929-2.  Google Scholar

show all references

References:
[1]

M. Aharon, M. Elad and A. Bruckstein, rmk-svd: An algorithm for designing overcomplete dictionaries for sparse representation, IEEE Trans. Signal Process., 54 (2006), 4311-4322.  doi: 10.1109/TSP.2006.881199.  Google Scholar

[2]

E. Arias-Castro, J. Salmon and R. Willett, Oracle inequalities and minimax rates for non-local means and related adaptive kernel-based methods, Siam Journal on Imaging Sciences, 5 (2012), 944-992.  doi: 10.1137/110859403.  Google Scholar

[3]

R. C. Bilcu and M. Vehvilainen, Fast nonlocal means for image denoising, In Proc. of SPIE Conf. on Digital Photography III, 6502 (2007), 65020R. doi: 10.1117/12.695079.  Google Scholar

[4]

J. Boulanger, C. Kervrann, P. Bouthemy, P. Elbau, J. B. Sibarita and J. Salamero, Patch-based nonlocal functional for denoising fluorescence microscopy image sequences, IEEE Transactions on Medical Imaging, 29 (2010), 442-454.  doi: 10.1109/TMI.2009.2033991.  Google Scholar

[5]

A. Buades, B. Coll and J. M. Morel, A review of image denoising algorithms, with a new one, Multiscale Model. Simul., 4 (2005), 490-530.  doi: 10.1137/040616024.  Google Scholar

[6]

A. Buades, B. Coll and J. M. Morel, The staircasing effect in neighborhood filters and its solution, IEEE Trans. Image Process., 15 (2006), 1499-1505.  doi: 10.1109/TIP.2006.871137.  Google Scholar

[7]

T. Buades, Y. Lou, J. M. Morel and Z. Tang, A note on multi-image denoising, In Int. workshop on Local and Non-Local Approximation in Image Processing, pages 1–15, August 2009. doi: 10.1109/LNLA.2009.5278408.  Google Scholar

[8]

P. Chatterjee and P. Milanfar, A generalization of non-local means via kernel regression, In Proc. of SPIE Conf. on Computational Imaging, Citeseer, 6814 (2008), 6814Op. doi: 10.1117/12.778615.  Google Scholar

[9]

K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Trans. Image Process., 16 (2007), 2080-2095.  doi: 10.1109/TIP.2007.901238.  Google Scholar

[10]

K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, Bm3d image denoising with shape-adaptive principal component analysis, In Proc. Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS 09), volume 49, Citeseer, 2009. Google Scholar

[11]

C. A. Deledalle, V. Duval and J. Salmon, Non-local methods with shape-adaptive patches (nlm-sap), Journal of Mathematical Imaging and Vision, 43 (2012), 103-120.  doi: 10.1007/s10851-011-0294-y.  Google Scholar

[12]

D. L. Donoho and J. M. Johnstone, Ideal spatial adaptation by wavelet shrinkage, Biometrika, 81 (1994), 425-455.  doi: 10.1093/biomet/81.3.425.  Google Scholar

[13]

V. Duval, J.-F. Aujol and Y. Gousseau, A bias-variance approach for the nonlocal means, SIAM Journal on Imaging Sciences, 4 (2011), 760-788.  doi: 10.1137/100790902.  Google Scholar

[14]

J. Q. Fan and I. Gijbels, Local Polynomial Modelling and Its Applications, Chapman & Hall, London, 1996.  Google Scholar

[15]

A. Foi, V. Katkovnik, K. Egiazarian and J. Astola, A novel anisotropic local polynomial estimator based on directional multiscale optimizations, In Proc. 6th IMA Int. Conf. Math. in Signal Process, pages 79–82, Citeseer. Google Scholar

[16]

D. K. Hammond and E. P. Simoncelli, Image modeling and denoising with orientation-adapted gaussian scale mixtures, IEEE Trans. Image Process., 17 (2008), 2089-2101.  doi: 10.1109/TIP.2008.2004796.  Google Scholar

[17]

K. Hirakawa and T. W. Parks, Image denoising using total least squares, IEEE Trans. Image Process., 15 (2006), 2730-2742.  doi: 10.1109/TIP.2006.877352.  Google Scholar

[18]

H. Hu, B. Li and Q. Liu, Removing mixture of gaussian and impulse noise by patch-basedweighted means, Journal of Scientific Computing, 67 (2016), 103-129.  doi: 10.1007/s10915-015-0073-9.  Google Scholar

[19]

J. Immerkaer, Fast noise variance estimation, Computer vision and image understanding, 64 (1996), 300-302.  doi: 10.1006/cviu.1996.0060.  Google Scholar

[20]

Q. Jin, I. Grama, C. Kervrann and Q. Liu, Nonlocal means and optimal weights for noise removal, SIAM Journal on Imaging Sciences, 10 (2017), 1878-1920.  doi: 10.1137/16M1080781.  Google Scholar

[21]

Q. Jin, I. Grama and Q. Liu, Removing gaussian noise by optimization of weights in non-local means, preprint, arXiv: 1109.5640. Google Scholar

[22]

Q. Jin, I. Grama and Q. Liu, A new poisson noise filter based on weights optimization, Journal of Scientific Computing, 58 (2014), 548-573.  doi: 10.1007/s10915-013-9743-7.  Google Scholar

[23]

V. Karnati, M. Uliyar and S. Dey, Fast non-local algorithm for image denoising, In IEEE International Conference on Image Processing (ICIP), 2009 16th, pages 3873–3876, IEEE, 2009. doi: 10.1109/ICIP.2009.5414044.  Google Scholar

[24]

V. Katkovnik, A. Foi, K. Egiazarian and J. Astola, Directional varying scale approximations for anisotropic signal processing, In Proc. XII European Signal Proc. Conf., EUSIPCO 2004, Vienna, pages 101–104, 2004. Google Scholar

[25]

V. Katkovnik, A. Foi, K. Egiazarian and J. Astola, From local kernel to nonlocal multiple-model image denoising, Int. J. Comput. Vis., 86 (2010), 1-32.  doi: 10.1007/s11263-009-0272-7.  Google Scholar

[26]

C. Kervrann and J. Boulanger, Optimal spatial adaptation for patch-based image denoising, IEEE Trans. Image Process., 15 (2006), 2866-2878.  doi: 10.1109/TIP.2006.877529.  Google Scholar

[27]

C. Kervrann and J. Boulanger, Local adaptivity to variable smoothness for exemplar-based image regularization and representation, Int. J. Comput. Vis., 79 (2008), 45-69.  doi: 10.1007/s11263-007-0096-2.  Google Scholar

[28]

M. Lebrun, A. Buades and J. M. Morel, Implementation of the "Non-Local Bayes" (NL-bayes) image denoising algorithm, Image Processing On Line, 2013 (2013), 1-42.   Google Scholar

[29]

M. Lebrun, A. Buades and J. M. Morel, A nonlocal bayesian image denoising algorithm, SIAM Journal on Imaging Sciences, 6 (2013), 1665-1688.  doi: 10.1137/120874989.  Google Scholar

[30]

A. Levin and B. Nadler, Natural image denoising: Optimality and inherent bounds, In Computer Vision and Pattern Recognition, pages 2833–2840, 2011. doi: 10.1109/CVPR.2011.5995309.  Google Scholar

[31]

B. Li, Q. Liu, J. Xu and X. Luo, A new method for removing mixed noises, Science China Information Sciences, 54 (2011), 51-59.  doi: 10.1007/s11432-010-4128-0.  Google Scholar

[32]

Y. Lou, X. Zhang, S. Osher and A. Bertozzi, Image recovery via nonlocal operators, J. Sci. Comput., 42 (2010), 185-197.  doi: 10.1007/s10915-009-9320-2.  Google Scholar

[33]

M. Mahmoudi and G. Sapiro, Fast image and video denoising via nonlocal means of similar neighborhoods, IEEE Signal. Proc. Let., 12 (2005), 839-842.  doi: 10.1109/LSP.2005.859509.  Google Scholar

[34]

A. Maleki, M. Narayan and R. Baraniuk, Suboptimality of Nonlocal Means on Images with Sharp Edges, In Annual Allerton Conference on Communication, Control, and Computing, 2011. Google Scholar

[35]

A. Maleki, M. Narayan and R. Baraniuk, Anisotropic nonlocal means denoising, Applied and Computational Harmonic Analysis, 35 (2013), 452-482.  doi: 10.1016/j.acha.2012.11.003.  Google Scholar

[36]

J. Polzehl and V. Spokoiny, Propagation-separation approach for local likelihood estimation, Probab. Theory Rel. Fields, 135 (2006), 335-362.  doi: 10.1007/s00440-005-0464-1.  Google Scholar

[37]

S. Roth and M. J. Black, Fields of experts, Int. J. Comput. Vision, 82 (2009), 205-229.  doi: 10.1007/s11263-008-0197-6.  Google Scholar

[38]

J. Salmon and E. Le Pennec, Nl-means and aggregation procedures, In IEEE Int. Conf. Image Process. (ICIP), pages 2977–2980. IEEE, 2009. doi: 10.1109/ICIP.2009.5414512.  Google Scholar

[39]

N. A. Thacker, P. A. Bromiley and J. V. Manjonb, A quantitative theory of the non-local means algorithm, In Proc. MIUA 2008, Dundee, Scotland, pages 174–178. Citeseer, 2008. Google Scholar

[40]

C. Tomasi and R. Manduchi, Bilateral filtering for gray and color images, In Proc. Int. Conf. Computer Vision, pages 839–846, 1998. doi: 10.1109/ICCV.1998.710815.  Google Scholar

[41]

D. Van De Ville and M. Kocher, Non-local means with dimensionality reduction and sure-based parameter selection, IEEE Trans. Image Process., 20 (2010), 2683-2690.  doi: 10.1109/TIP.2011.2121083.  Google Scholar

[42]

R. Vignesh, B. T. Oh and C. C. J. Kuo, Fast non-local means (nlm) computation with probabilistic early termination, IEEE Signal. Proc. Let., 17 (2010), 277-280.  doi: 10.1109/LSP.2009.2038956.  Google Scholar

[43]

Y. Q. Wang, The implementation of sure guided piecewise linear image denoising, Image Processing On Line, 2013 (2013), 43-67.  doi: 10.5201/ipol.2013.52.  Google Scholar

[44]

L. P. Yaroslavsky, Digital Picture Processing. An Introduction, In Springer-Verlag, Berlin, 1985. doi: 10.1007/978-3-642-81929-2.  Google Scholar

Figure 4.  The restored image (left) and it its square error (right) with different similarity patch sizes $d = 7, 9, 21, 41$ and the same search window size $D = 13.$ The original image Lena was polluted by a Gaussian noise with $\sigma = 20$
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Figure 1.  Approximation of $H = \sqrt{a_0 \sigma^2 + b_0}$ (red line) by $H = a \sigma +b$ (black line) with $a = 0.4$, $b = 2$, $a_0 = 0.2,$ $b_0 = 10$
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Figure 2.  The evolution of the PSNR as a function of the parameter $H$
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Figure 3.  The evolution of the PSNR as a function of the size of similarity patches $d$
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Figure 5.  The restored image (left) and it its square error (right) with different similarity patch sizes $d = 7, 9, 21, 41$ and the same search window size $D = 13.$ The original image Boat was polluted by a Gaussian noise with $\sigma = 20$
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Figure 6.  The restored image (left) and it its square error (right) with different similarity patch sizes $d = 7, 9, 21, 41$ and the same search window size $D = 13.$ The original image Peppers was polluted by a Gaussian noise with $\sigma = 20$
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Figure 7.  The evolution of the PSNR as a function of the size of a search window $D$
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Figure 8.  The restored image (left) and it its square error (right) with the same similarity patch size $d = 21$ and different search window sizes $D = 9,13, 17, 21$. The original image Boat was polluted by a Gaussian noise with $\sigma = 20$
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Figure 9.  The restored image (left) and it its square error (right) with the same similarity patch size $d = 21$ and different search window sizes $D = 9,13, 17, 21$. The original image Peppers was polluted by a Gaussian noise with $\sigma = 20$
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Figure 10.  The restored image (left) and it its square error (right) with the same similarity patch size $d = 21$ and different search window sizes $D = 9,13, 17, 21$. The original image Lena was polluted by a Gaussian noise with $\sigma = 20$
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$512\times 512$ image">Figure 11.  Denoising results with the "Lena" $512\times 512$ image
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$512\times 512$ image">Figure 12.  Denoising results with the "Boat" $512\times 512$ image
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$256\times 256$ image">Figure 13.  Denoising results with the "Pepper" $256\times 256$ image
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Table 1.  The PSNR when the oracle estimator $u^{\ast }$ is applied with different values of $D$
Image Size Lena
$512\times512$		 Barbara
$512\times512$		 Boats
$512\times512$		 House
$256\times256$		 Peppers
$256\times256$
$\sigma /PSNR$ 10/28.12db 10/28.12db 10/28.12db 10/28.11db 10/28.11db
$9 \times 9$ 38.98db 37.26db 37.66db 38.93db 37.85db
$11 \times 11$ 40.12db 38.49db 38.80db 40.04db 38.85db
$13 \times 13$ 41.09db 39.55db 39.78db 40.98db 39.64db
$15 \times 15$ 41.92db 40.45db 40.63db 41.77db 40.39db
$17 \times 17$ 42.64db 41.23db 41.39db 42.40db 41.00db
$19 \times 19$ 43.29db 41.93db 42.06db 43.06db 41.58db
$21 \times 21$ 43.88db 42.57db 42.67db 43.61db 42.14db
$\sigma /PSNR$ 20/22.11db 20/22.11db 20/22.11db 20/28.12db 20/28.12db
$9 \times 9 $ 33.61db 31.91db 32.32db 33.72db 32.62db
$11 \times 11$ 34.78db 33.20db 33.49db 34.92db 33.65db
$13 \times 13$ 35.80db 34.28db 34.49db 35.98db 34.51db
$15 \times 15$ 36.69db 35.22db 35.40db 36.80db 35.26db
$17 \times 17$ 37.48db 36.05db 36.20db 37.48db 35.89db
$19 \times 19$ 38.17db 36.74db 36.90db 38.07db 36.45db
$21 \times 21$ 38.80db 37.40db 37.54db 38.67db 36.98db
$\sigma /PSNR$ 30/18.60db 30/18.60db 30/18.60db 30/18.61db 20/28.12db
$9 \times 9$ 30.65db 28.89db 29.25db 30.69db 29.51db
$11 \times 11$ 31.83db 30.23db 30.45db 31.90db 30.51db
$13 \times 13$ 32.85db 31.33db 31.49db 32.92db 31.34db
$15 \times 15$ 33.74db 32.27db 32.37db 33.76db 32.08db
$17 \times 17$ 34.50db 33.09db 33.16db 34.48db 32.74db
$19 \times 19$ 35.20db 33.81db 33.85db 35.13db 33.32db
$21 \times 21$ 35.79db 34.46db 34.48db 35.71db 33.85db
Table Options

Download as excel

Image Size Lena
$512\times512$		 Barbara
$512\times512$		 Boats
$512\times512$		 House
$256\times256$		 Peppers
$256\times256$
$\sigma /PSNR$ 10/28.12db 10/28.12db 10/28.12db 10/28.11db 10/28.11db
$9 \times 9$ 38.98db 37.26db 37.66db 38.93db 37.85db
$11 \times 11$ 40.12db 38.49db 38.80db 40.04db 38.85db
$13 \times 13$ 41.09db 39.55db 39.78db 40.98db 39.64db
$15 \times 15$ 41.92db 40.45db 40.63db 41.77db 40.39db
$17 \times 17$ 42.64db 41.23db 41.39db 42.40db 41.00db
$19 \times 19$ 43.29db 41.93db 42.06db 43.06db 41.58db
$21 \times 21$ 43.88db 42.57db 42.67db 43.61db 42.14db
$\sigma /PSNR$ 20/22.11db 20/22.11db 20/22.11db 20/28.12db 20/28.12db
$9 \times 9 $ 33.61db 31.91db 32.32db 33.72db 32.62db
$11 \times 11$ 34.78db 33.20db 33.49db 34.92db 33.65db
$13 \times 13$ 35.80db 34.28db 34.49db 35.98db 34.51db
$15 \times 15$ 36.69db 35.22db 35.40db 36.80db 35.26db
$17 \times 17$ 37.48db 36.05db 36.20db 37.48db 35.89db
$19 \times 19$ 38.17db 36.74db 36.90db 38.07db 36.45db
$21 \times 21$ 38.80db 37.40db 37.54db 38.67db 36.98db
$\sigma /PSNR$ 30/18.60db 30/18.60db 30/18.60db 30/18.61db 20/28.12db
$9 \times 9$ 30.65db 28.89db 29.25db 30.69db 29.51db
$11 \times 11$ 31.83db 30.23db 30.45db 31.90db 30.51db
$13 \times 13$ 32.85db 31.33db 31.49db 32.92db 31.34db
$15 \times 15$ 33.74db 32.27db 32.37db 33.76db 32.08db
$17 \times 17$ 34.50db 33.09db 33.16db 34.48db 32.74db
$19 \times 19$ 35.20db 33.81db 33.85db 35.13db 33.32db
$21 \times 21$ 35.79db 34.46db 34.48db 35.71db 33.85db
Table 2.  Comparison between the Non-Local Means filter with the parameters in Buades et al. [5] and our parameters
Image Size Lena
$512\times512$		 Barbara
$512\times512$		 Boats
$512\times512$		 House
$256\times256$		 Peppers
$256\times256$
$\sigma/PSNR$ 10/28.12db 10/28.12db 10/28.12db 10/28.11db 10/28.11db
PSNR/Buades et al. [5] 34.99db 33.82db 32.85db 35.50db 33.13db
PSNR/Ours 35.22db 33.55db 33.00db 35.35db 33.16db
$\Delta$PSNR 0.23db -0.27db 0.15db -0.15db 0.03db
$\sigma/PSNR$ 20/22.11db 20/22.11db 20/22.11db 20/28.12db 20/28.12db
PSNR/Buades et al. [5] 31.51db 30.38db 29.32db 32.51db 29.73db
PSNR/Ours 32.41db 30.62db 30.02db 32.57db 30.30db
$\Delta$PSNR 0.82db 0.24db 0.70db 0.08db 0.57db
$\sigma/PSNR$ 30/18.60db 30/18.60db 30/18.60db 30/18.61db 30/18.61db
PSNR/Buades et al. [5] 28.86db 27.65db 27.38db 29.17db 27.67db
PSNR/Ours 30.20db 28.06db 28.60db 30.49db 28.28db
$\Delta$PSNR 1.34db 0.41db 1.22db 1.32db 0.61db
Table Options

Download as excel

Image Size Lena
$512\times512$		 Barbara
$512\times512$		 Boats
$512\times512$		 House
$256\times256$		 Peppers
$256\times256$
$\sigma/PSNR$ 10/28.12db 10/28.12db 10/28.12db 10/28.11db 10/28.11db
PSNR/Buades et al. [5] 34.99db 33.82db 32.85db 35.50db 33.13db
PSNR/Ours 35.22db 33.55db 33.00db 35.35db 33.16db
$\Delta$PSNR 0.23db -0.27db 0.15db -0.15db 0.03db
$\sigma/PSNR$ 20/22.11db 20/22.11db 20/22.11db 20/28.12db 20/28.12db
PSNR/Buades et al. [5] 31.51db 30.38db 29.32db 32.51db 29.73db
PSNR/Ours 32.41db 30.62db 30.02db 32.57db 30.30db
$\Delta$PSNR 0.82db 0.24db 0.70db 0.08db 0.57db
$\sigma/PSNR$ 30/18.60db 30/18.60db 30/18.60db 30/18.61db 30/18.61db
PSNR/Buades et al. [5] 28.86db 27.65db 27.38db 29.17db 27.67db
PSNR/Ours 30.20db 28.06db 28.60db 30.49db 28.28db
$\Delta$PSNR 1.34db 0.41db 1.22db 1.32db 0.61db
Table 3.  Comparison of the Non-Local Means filter with our choice of parameters and other algorithms. By $^*$ we mark the algorithms for which the results were reported by their authors
Images Sizes Lena
$512 \times 512$		 Barbara
$512 \times 512$		 Boat
$512 \times 512$		 House
$256 \times 256$		 Peppers
$256 \times 256$
$\sigma$ Method PSNR PSNR PSNR PSNR PSNR
20 Non-Local Means with $D=13$ and $d=21$ 32.41db 30.62db 30.02db 32.57db 30.30db
Buades et al. [5] 31.51db 30.38db 29.32db 32.51db 29.73db
Deledalle et al. [11]$^*$ 31.92db 30.41db 29.67db 32.49db 30.77db
Katkovnik et al. [24]$^*$ 30.74db 27.38db 29.03db 31.24db 29.58db
Foi et al. [15]$^*$ 31.43db 27.90db 39.61db 31.84db 30.30db
Roth et al. [37]$^*$ 31.89db 28.28db 29.86db 32.29db 30.47db
Hirkawa et al. [17]$^*$ 32.69db 31.06db 30.25db 32.58db 30.21db
Kervrann et al. [27] 32.64db 30.37db 30.12db 32.90db 30.59db
Jin et al. [21] 32.68db 31.04db 30.30db 32.83db 30.61db
Hammond et al. [16] 32.81db 30.76db 30.41db 32.52db 30.40db
Aharon et al. [1]$^*$ 32.39db 30.84db 30.39db 33.10db 30.80db
Arias-Castro et al. [2]$^*$ 31.17db 29.47db 28.62db 31.91db 29.21db
Van De Ville et Kocher [41]$^*$ 31.33db 29.48db 29.82db 31.80db 29.65db
Dabov et al. [9] 33.05db 31.78db 30.88db 33.77db 31.29db
Lebrun et al. [28,29] 32.91db 31.51db 30.71db 33.55db 31.22db
Table Options

Download as excel

Images Sizes Lena
$512 \times 512$		 Barbara
$512 \times 512$		 Boat
$512 \times 512$		 House
$256 \times 256$		 Peppers
$256 \times 256$
$\sigma$ Method PSNR PSNR PSNR PSNR PSNR
20 Non-Local Means with $D=13$ and $d=21$ 32.41db 30.62db 30.02db 32.57db 30.30db
Buades et al. [5] 31.51db 30.38db 29.32db 32.51db 29.73db
Deledalle et al. [11]$^*$ 31.92db 30.41db 29.67db 32.49db 30.77db
Katkovnik et al. [24]$^*$ 30.74db 27.38db 29.03db 31.24db 29.58db
Foi et al. [15]$^*$ 31.43db 27.90db 39.61db 31.84db 30.30db
Roth et al. [37]$^*$ 31.89db 28.28db 29.86db 32.29db 30.47db
Hirkawa et al. [17]$^*$ 32.69db 31.06db 30.25db 32.58db 30.21db
Kervrann et al. [27] 32.64db 30.37db 30.12db 32.90db 30.59db
Jin et al. [21] 32.68db 31.04db 30.30db 32.83db 30.61db
Hammond et al. [16] 32.81db 30.76db 30.41db 32.52db 30.40db
Aharon et al. [1]$^*$ 32.39db 30.84db 30.39db 33.10db 30.80db
Arias-Castro et al. [2]$^*$ 31.17db 29.47db 28.62db 31.91db 29.21db
Van De Ville et Kocher [41]$^*$ 31.33db 29.48db 29.82db 31.80db 29.65db
Dabov et al. [9] 33.05db 31.78db 30.88db 33.77db 31.29db
Lebrun et al. [28,29] 32.91db 31.51db 30.71db 33.55db 31.22db
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