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A statistical approach to adaptive search region selection for NLM-based image denoising algorithm

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Abstract

Non-local means (NLM) filtering is an effective and popular image denoising algorithm. It estimates the pixel by taking advantage of redundancy present in a whole image or in a predefined fixed search region. NLM algorithm using fixed size search region for all pixels is unable to preserve the important details such as edges and texture in an image due to inclusion of unrelated pixels in the averaging. The selection of variable size search region for each pixel in an image is a critical issue in NLM algorithm. This paper focuses on the selection of optimal size search region for each pixel according to the characteristics of the region. The proposed algorithm adaptively selects an optimal size search region for a pixel based on maximization of ratio of contribution of similar pixels to dissimilar pixels for different search regions centered on that pixel. Experimental results on standard natural test images show that the proposed algorithm performs consistently better than the conventional NLM and other state-of-the-art NLM variants in terms of PSNR (dB), SSIM and visual quality at various noise levels.

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Notes

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

References

  1. Berkovich H, Malah D, Barzohar M (2013) Non-local means denoising using a content-based search region and dissimilarity kernel. In Image and Signal Processing and Analysis (ISPA), 2013 8th International Symposium on

  2. Brownlee KA, Brownlee KA (1965) Statistical theory and methodology in science and engineering, vol. 150. Wiley, New York

    MATH  Google Scholar 

  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. Buades A, Coll B, Morel J-M (2010) Image denoising methods. A new nonlocal principle. SIAM Rev 52(1):113–147

    Article  MathSciNet  MATH  Google Scholar 

  5. Buades A, Coll B, Morel J-M (2011) Non-local means denoising. Image Process On Line 1. doi:10.5201/ipol.2011.bcm_nlm

  6. Dabov K, Foi A, Katkovnik V, Egiazarian K (2007) Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans Image Process 16(8):2080–2095

    Article  MathSciNet  Google Scholar 

  7. de la Rosa Vargas JI, Villa JJ, Gonz E, Cortez J et al. (2016) A tour of nonlocal means techniques for image filtering. In 2016 International Conference on Electronics, Communications and Computers (CONIELECOMP)

  8. Efros AA, Leung TK (1999) Texture synthesis by non-parametric sampling. In Computer vision, 1999. The Proceedings of the Seventh IEEE International Conference on

  9. Gonzalez RC, Woods RE (2002) Digital image processing. Prentice Hall, Upper Saddle River

  10. 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 

  11. Kumar BKS (2013) Image denoising based on non-local means filter and its method noise thresholding. SIViP 7(6):1211–1227

    Article  Google Scholar 

  12. Lefkimmiatis S, Maragos P, Papandreou G (2009) Bayesian inference on multiscale models for Poisson intensity estimation: applications to photon-limited image denoising. IEEE Trans Image Process 18(8):1724–1741

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  14. Makitalo M, Foi A (2011) Optimal inversion of the Anscombe transformation in low-count Poisson image denoising. IEEE Trans Image Process 20(1):99–109

    Article  MathSciNet  MATH  Google Scholar 

  15. Makitalo M, Foi A (2013) Optimal inversion of the generalized Anscombe transformation for Poisson-Gaussian noise. IEEE Trans Image Process 22(1):91–103

    Article  MathSciNet  MATH  Google Scholar 

  16. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693

    Article  MATH  Google Scholar 

  17. Mallat S (1999) A wavelet tour of signal processing. Academic press, California

  18. Milanfar P (2013) A tour of modern image filtering: new insights and methods, both practical and theoretical. Signal Process Mag IEEE 30(1):106–128

    Article  Google Scholar 

  19. Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639

    Article  Google Scholar 

  20. Shreyamsha Kumar BK (2012) Image denoising based on gaussian/bilateral filter and its method noise thresholding. SIViP 7(6):1159–1172

    Article  Google Scholar 

  21. Tasdizen T (2009) Principal neighborhood dictionaries for nonlocal means image denoising. Image Processing IEEE Transactions on 18(12):2649–2660

    Article  MathSciNet  MATH  Google Scholar 

  22. Thacker NA, Manjon JV, Bromiley PA (2010) Statistical interpretation of non-local means. Comput Vis, IET 4(3):162–172

    Article  MathSciNet  Google Scholar 

  23. Tiku M (2004) Noncentral chi-square distribution. In Encyclopedia of statistical sciences. John Wiley & Sons, Inc. doi:10.1002/0471667196.ess1798.pub2

  24. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In Computer Vision, 1998. Sixth International Conference on

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

    Article  Google Scholar 

  26. Van De Ville D, Kocher M (2011) Nonlocal means with dimensionality reduction and SURE-based parameter selection. Image Processing, IEEE Transactions on 20(9):2683–2690

    Article  MathSciNet  MATH  Google Scholar 

  27. 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 

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

    Article  Google Scholar 

  29. Wu Y, Tracey B, Natarajan P, Noonan JP (2013b) Probabilistic non-local means. Signal Process Lett IEEE 20(8):763–766

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  32. Zhang B, Fadili JM, Starck J-L (2008) Wavelets, ridgelets, and curvelets for Poisson noise removal. IEEE Trans Image Process 17(7):1093–1108

    Article  MathSciNet  Google Scholar 

  33. Zhu S, Li Y, Li Y (2014) Two-stage non-local means filtering with adaptive smoothing parameter. Optik-Int J Light Electron Optics 125(23):7040–7044

    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. A statistical approach to adaptive search region selection for NLM-based image denoising algorithm. Multimed Tools Appl 77, 549–566 (2018). https://doi.org/10.1007/s11042-016-4227-z

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  • DOI: https://doi.org/10.1007/s11042-016-4227-z

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