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Erschienen in: Programming and Computer Software 4/2019

01.07.2019

A Nonlocal Image Denoising Algorithm Using the Structural Similarity Metric

verfasst von: A. A. Dovganich, A. S. Krylov

Erschienen in: Programming and Computer Software | Ausgabe 4/2019

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Abstract

A new image denoising algorithm is proposed. It is a version of the nonlocal means (NLM) algorithm and uses a metric based on the CMCS modification of the structural similarity index (SSIM). The potentials of this metric for constructing the weighting function in the NLM method using the decomposition of this metric into components and specifying a physically justified weighting function for each component are demonstrated. The results produced by the modified method are compared with the results produced by the basic NLM algorithm, which uses the metrics L2 and SSIM for calculating the metric weights.

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Literatur
1.
Zurück zum Zitat Buades, A., Coll, B., and Morel, J.M., A review of image denoising algorithms, with a new one, Multiscale Model. Simul., 2005, vol. 4, no. 2, pp. 490–530.MathSciNetCrossRefMATH Buades, A., Coll, B., and Morel, J.M., A review of image denoising algorithms, with a new one, Multiscale Model. Simul., 2005, vol. 4, no. 2, pp. 490–530.MathSciNetCrossRefMATH
2.
Zurück zum Zitat Gonzalez, R.C. and Woods, R.E., Digital Image Processing, Upper Saddle River, N. J.: Prentice Hall, 2004. Gonzalez, R.C. and Woods, R.E., Digital Image Processing, Upper Saddle River, N. J.: Prentice Hall, 2004.
3.
Zurück zum Zitat Yaroslavskii, L.P., Digital Signal Processing in Optics and Holography: Introduction to Digital Optics, Moscow: Radio i Svyaz’, 1987 Yaroslavskii, L.P., Digital Signal Processing in Optics and Holography: Introduction to Digital Optics, Moscow: Radio i Svyaz’, 1987
4.
Zurück zum Zitat Cruz, C. et al. Nonlocality-reinforced convolutional neural networks for image denoising, 2018. arXiv:1803. 02112 Cruz, C. et al. Nonlocality-reinforced convolutional neural networks for image denoising, 2018. arXiv:1803. 02112
5.
Zurück zum Zitat Weickert, J., Anisotropic Diffusion in Image Processing, ECMI Series, Stuttgart: Teubner, 1998.MATH Weickert, J., Anisotropic Diffusion in Image Processing, ECMI Series, Stuttgart: Teubner, 1998.MATH
6.
Zurück zum Zitat Perona, P. and Malik, J., Scale-space and edge detection using anisotropic diffusion, IEEE Trans. Pattern Anal. Mach. Intell., 1990, vol. 12, no. 7, pp. 629–639.CrossRef Perona, P. and Malik, J., Scale-space and edge detection using anisotropic diffusion, IEEE Trans. Pattern Anal. Mach. Intell., 1990, vol. 12, no. 7, pp. 629–639.CrossRef
7.
Zurück zum Zitat Dovganich, A.A., Krylov, A.S., ad Yurin, D.V., Nonlocal means algorithm with a metric based on the modified structural similarity index, 28th International Conference on Computer Graphics and Machine Vision (GraphiCon), 2018, pp. 254–258. Dovganich, A.A., Krylov, A.S., ad Yurin, D.V., Nonlocal means algorithm with a metric based on the modified structural similarity index, 28th International Conference on Computer Graphics and Machine Vision (GraphiCon), 2018, pp. 254–258.
8.
Zurück zum Zitat Buades, A. and Morel, J.M., A non-local algorithm for image denoising, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, Vol. 2, pp. 60–65. Buades, A. and Morel, J.M., A non-local algorithm for image denoising, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, Vol. 2, pp. 60–65.
9.
Zurück zum Zitat Wang, S., Xia, Y., Liu, Q., Luo, J., Zhu, Y., and Feng, D., Gabor feature based nonlocal means filter for textured image denoising, J. Visual Commun. Image Representation, 2012, vol. 23, no. 7. pp. 1008–1018.CrossRef Wang, S., Xia, Y., Liu, Q., Luo, J., Zhu, Y., and Feng, D., Gabor feature based nonlocal means filter for textured image denoising, J. Visual Commun. Image Representation, 2012, vol. 23, no. 7. pp. 1008–1018.CrossRef
10.
Zurück zum Zitat Mamaev, N.V., Lukin, A.S., and Yurin, D.V., HeNLM–LA: A locally adaptive non-local means algorithm based on hermite functions expansion, Program. Comput. Software, 2014, vol. 40, no, 4, pp. 199–207.MathSciNetCrossRef Mamaev, N.V., Lukin, A.S., and Yurin, D.V., HeNLM–LA: A locally adaptive non-local means algorithm based on hermite functions expansion, Program. Comput. Software, 2014, vol. 40, no, 4, pp. 199–207.MathSciNetCrossRef
11.
Zurück zum Zitat Manzanera, A., Local Jet based similarity for NL-means filtering,), 20th IEEE International Conference on Pattern Recognition (ICPR), 2010, pp. 2668–2671. Manzanera, A., Local Jet based similarity for NL-means filtering,), 20th IEEE International Conference on Pattern Recognition (ICPR), 2010, pp. 2668–2671.
12.
Zurück zum Zitat Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K., Image denoising by sparse 3D transform-domain collaborative filtering, IEEE Trans. Image Process., 2007, vol. 16, no. 8, pp. 2080–2095.MathSciNetCrossRef Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K., Image denoising by sparse 3D transform-domain collaborative filtering, IEEE Trans. Image Process., 2007, vol. 16, no. 8, pp. 2080–2095.MathSciNetCrossRef
13.
Zurück zum Zitat Zhang, K., Zuo, W., Chen, Y., Meng, D., and Zhang, L., Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising, IEEE Trans. Image Process., 2017, vol. 26, no. 7, pp. 3142–3155.MathSciNetCrossRefMATH Zhang, K., Zuo, W., Chen, Y., Meng, D., and Zhang, L., Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising, IEEE Trans. Image Process., 2017, vol. 26, no. 7, pp. 3142–3155.MathSciNetCrossRefMATH
14.
Zurück zum Zitat Jin, K.H., McCann, M.T., Froustey, E., and Unser, M., Deep convolutional neural network for inverse problems in imaging, IEEE Trans. Image Process., 2017, vol. 26, no. 9, pp. 4509–4522.MathSciNetCrossRefMATH Jin, K.H., McCann, M.T., Froustey, E., and Unser, M., Deep convolutional neural network for inverse problems in imaging, IEEE Trans. Image Process., 2017, vol. 26, no. 9, pp. 4509–4522.MathSciNetCrossRefMATH
15.
Zurück zum Zitat Palubinskas, G., Mystery behind similarity measures MSE and SSIM, IEEE International Conference on Image Processing (ICIP), 2014, pp. 575–579. Palubinskas, G., Mystery behind similarity measures MSE and SSIM, IEEE International Conference on Image Processing (ICIP), 2014, pp. 575–579.
16.
Zurück zum Zitat Wang, Z., Bovik, A., Sheikh, H., and Simoncelli, E.P., Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process., 2004, vol. 13, no., pp. 600–612. Wang, Z., Bovik, A., Sheikh, H., and Simoncelli, E.P., Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process., 2004, vol. 13, no., pp. 600–612.
17.
Zurück zum Zitat Wang, Z. and A.C. Bovik, A.C., Mean squared error: Love it or leave it? – A new look at signal fidelity measures, IEEE Signal Process. Magazine, 2009, vol. 26, no. 1, pp. 98–117.CrossRef Wang, Z. and A.C. Bovik, A.C., Mean squared error: Love it or leave it? – A new look at signal fidelity measures, IEEE Signal Process. Magazine, 2009, vol. 26, no. 1, pp. 98–117.CrossRef
18.
Zurück zum Zitat Rehman, A. and Wang, Z., SSIM-based non-local means image denoising, 18th IEEE International Conference on Image Processing (ICIP), 2011, pp. 217–220. Rehman, A. and Wang, Z., SSIM-based non-local means image denoising, 18th IEEE International Conference on Image Processing (ICIP), 2011, pp. 217–220.
19.
Zurück zum Zitat Ponomarenko, N. et al., Color image database TID2013: Peculiarities and preliminary results, Proc. of the 4th IEEE European Workshop on Visual Information Processing (EUVIP), 2013, pp. 106–111. Ponomarenko, N. et al., Color image database TID2013: Peculiarities and preliminary results, Proc. of the 4th IEEE European Workshop on Visual Information Processing (EUVIP), 2013, pp. 106–111.
Metadaten
Titel
A Nonlocal Image Denoising Algorithm Using the Structural Similarity Metric
verfasst von
A. A. Dovganich
A. S. Krylov
Publikationsdatum
01.07.2019
Verlag
Pleiades Publishing
Erschienen in
Programming and Computer Software / Ausgabe 4/2019
Print ISSN: 0361-7688
Elektronische ISSN: 1608-3261
DOI
https://doi.org/10.1134/S0361768819040029

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