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Grey relational analysis based adaptive smoothing parameter for non-local means image denoising

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Abstract

In non-local means (NLM) algorithm used for suppression of noise in digital images, the choice of smoothing or decay parameter is a critical issue, which affects the performance of NLM algorithm by influencing the amount of smoothing. Generally, the smoothing parameter in NLM algorithm is kept fixed for all pixels in an image, which provides blurring effects near important image details such as edges, textures etc. in an image. This paper presents a grey relational analysis based adaptive non-local means (GRANLM) algorithm to select an adaptive smoothing parameter for each pixel. It considers the grade of relations between reference patch and adjacent patches in a defined region centred at pixel using grey relational analysis (GRA). Experimental results on various standard images for different noise levels show that the proposed algorithm outperforms the traditional NLM algorithm and other NLM variants in terms of peak signal to noise ratio (PSNR), structural similarity index measure (SSIM), visual quality and method noise.

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Notes

  1. Availabel link: https://www.cs.cmu.edu/~cil/v-images.html

  2. Availabel link [7]: https://mmlab.science.unitn.it/RAISE/

References

  1. Bovik AC (2009) The essential guide to image processing. Academic Press, Cambridge

    Google Scholar 

  2. Buades A, Coll B, Morel J-M (2005) A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol 2. IEEE, pp 60–65

  3. Buades A, Coll B, Morel J-M (2005) A review of image denoising algorithms, with a new one. Multiscale Model Simul 4(2):490–530

    Article  MathSciNet  MATH  Google Scholar 

  4. Cattin DP (2013) Image restoration: Introduction to signal and image processing, MIAC, University of Basel, 11

  5. Chen Q, Sun Q-S, Xia D-S (2010) Homogeneity similarity based image denoising. Pattern Recognit 43(12):4089–4100

    Article  MATH  Google Scholar 

  6. Dai T, Lu W, Wang W, Wang J, Xia S-T (2017) Entropy-based bilateral filtering with a new range kernel. Signal Process 137:223–234

    Article  Google Scholar 

  7. Dang-nguyen D-T, Pasquini C, Conotter V, Boato G (2015) RAISE: a raw images dataset for digital image forensics. In: Proceedings of the 6th ACM Multimedia Systems Conference, MMSys ’15. ACM, New York, pp 219–224

  8. de la Rosa Vargas JI, Villa JJ, González E, Cortez J (2016) A tour of nonlocal means techniques for image filtering. In: 2016 International Conference on Electronics, Communications and Computers (CONIELECOMP), pp 32–39

  9. Feng D, Yan J, Huang P (2006) A novel de-noise method based on the grey relational analysis. In: 2006 IEEE International Conference on Information Acquisition, pp 1385–1389

  10. Ghosh S, Mandal AK, Chaudhury KN (2017) Pruned non-local means. IET Image Process 11(5):317–323

    Article  Google Scholar 

  11. Gonzalez RC, Woods RE (2006) Digital image processing, 3rd Edition. Prentice-Hall, Inc., Upper Saddle River

    Google Scholar 

  12. Hu J, Luo Y-P (2013) Non-local means algorithm with adaptive patch size and bandwidth. Optik-Int J Light Electron Opt 124(22):5639–5645

    Article  Google Scholar 

  13. Jain P, Tyagi V (2014) A survey of edge-preserving image denoising methods. Inf Syst Front 18(1):159–170

    Article  Google Scholar 

  14. Ju-Long D (1982) Control problems of grey systems. Syst Control Lett 1 (5):288–294

    Article  MathSciNet  MATH  Google Scholar 

  15. Kuo Y, Yang T, Huang G-W (2008) The use of grey relational analysis in solving multiple attribute decision-making problems. Comput Ind Eng 55(1):80–93

    Article  Google Scholar 

  16. Li H, Suen CY (2016) A novel non-local means image denoising method based on grey theory. Pattern Recogn 49:237–248

    Article  Google Scholar 

  17. Mahmoudi M, Sapiro G (2005) Fast image and video denoising via nonlocal means of similar neighborhoods. IEEE Signal Process Lett 12:839–842

    Article  Google Scholar 

  18. Milanfar P (2013) A tour of modern image filtering: new insights and methods, both practical and theoretical. IEEE Signal Proc Mag 30:106–128

    Article  Google Scholar 

  19. Nguyen MP, Chun SY (2017) Bounded self-weights estimation method for non-local means image denoising using minimax estimators. IEEE Trans Image Process 26(4):1637–1649

    Article  MathSciNet  Google Scholar 

  20. Pardo Á (2009) Analysis of non local image denoising methods, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol 5856 LNCS, pp 103–110

  21. Plataniotis KN, Androutsos D, Venetsanopoulos AN (1999) Adaptive fuzzy systems for multichannel signal processing. Proc IEEE 87:1601–1622

    Article  Google Scholar 

  22. Qiao B, Jin L, Yang Y (2016) An adaptive algorithm for grey image edge detection based on grey correlation analysis. In: 2016 12th International Conference on Computational Intelligence and Security (CIS), pp 470–474

  23. Rajpoot N, Butt I (2012) A multiresolution framework for local similarity based image denoising. Pattern Recogn 45(8):2938–2951

    Article  Google Scholar 

  24. Salmon J, Strozecki Y (2010) From patches to pixels in non-local methods: weighted-average reprojection. In: 2010 17th IEEE International Conference on Image Processing (ICIP). IEEE, pp 1929–1932

  25. Siddeq MM, Rodrigues MA (2014) A novel image compression algorithm for high resolution 3d reconstruction. 3D Res 5(2):7

    Article  Google Scholar 

  26. Tasdizen T (2009) Principal neighborhood dictionaries for nonlocal means image denoising. IEEE Trans Image Process 18(12):2649–2660

    Article  MathSciNet  MATH  Google Scholar 

  27. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: 1998 Sixth International Conference on Computer Vision. IEEE, pp 839–846

  28. Van De Ville D, Kocher M (2009) Sure-based non-local means. IEEE Signal Process Lett 16(11):973–976

    Article  Google Scholar 

  29. Verma R, Pandey R (2015) Non local means algorithm with adaptive isotropic search window size for image denoising. In: 2015 Annual IEEE India Conference (INDICON). IEEE, pp 1–5

  30. Verma R, Pandey R (2017) Adaptive selection of search region for nlm based image denoising. Optik - Int J Light Electron Opt 147(Supplement C):151–162

    Article  Google Scholar 

  31. Verma R, Pandey R (2018) A statistical approach to adaptive search region selection for nlm-based image denoising algorithm. Multimed Tool Appl 77:549–566

    Article  Google Scholar 

  32. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13 (4):600–612

    Article  Google Scholar 

  33. Wu Y, Tracey B, Natarajan P, Noonan JP (2013) James–stein type center pixel weights for non-local means image denoising. IEEE Signal Process Lett 20 (4):411–414

    Article  Google Scholar 

  34. Xiao F, Zhang Y (2011) A comparative study on thresholding methods in wavelet-based image denoising. Procedia Eng 15:3998–4003

    Article  Google Scholar 

  35. Zeng W, Lu X (2011) Region-based non-local means algorithm for noise removal. Electron Lett 47(20):1125–1127

    Article  Google Scholar 

  36. Zeng W, Du Y, Hu C (2017) Noise suppression by discontinuity indicator controlled non-local means method. Multimed Tool Appl 76(11):13239–13253

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology, Kurukshetra, 136119, India

    Rajiv Verma & Rajoo Pandey

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  1. Rajiv Verma
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  2. Rajoo Pandey
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Correspondence to Rajiv Verma.

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Verma, R., Pandey, R. Grey relational analysis based adaptive smoothing parameter for non-local means image denoising. Multimed Tools Appl 77, 25919–25940 (2018). https://doi.org/10.1007/s11042-018-5828-5

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