The Visual Computer

, Volume 34, Issue 4, pp 575–587 | Cite as

Natural image noise level estimation based on local statistics for blind noise reduction

  • Asem Khmag
  • Abd Rahman Ramli
  • S. A. R. Al-haddad
  • Noraziahtulhidayu Kamarudin
Original Article


This study proposes an automatic noise estimation method based on local statistics for additive white Gaussian noise. Noise estimation is an important process in digital imaging systems. For example, the performance of an image denoising algorithm can be significantly degraded because of poor noise level estimation. Most of the literature on the subject tends to use the true noise level of a noisy image when suppressing noise artifacts. Moreover, even with the given true noise level, these denoising techniques still cannot attain the best result, particularly for images with complicated details. In this study, a patch-based estimation technique is used to estimate for noise level and applies it to the proposed blind image denoising algorithm. Our approach includes selecting low-rank sub-image with removing high-frequency components from the contaminated image. This selection is according to the gradients of patches with the same statistics. Consequently, we need to estimate the noise level from the selected patches using principal component analysis (PCA). For blind denoising applications, the proposed denoising algorithm integrates the undecimated wavelet-based denoising algorithms and PCA to develop the subjective and objective qualities of the observed image, which result from filtering processes. Experiment results depict that the suggested algorithm performs efficiently over a wide range of visual contents and noise conditions, as well as in additive noise. Associated with different conventional noise estimators, the proposed algorithm yields the best performance, higher-quality images, and faster running speed.


Gaussian noise Noise estimation Image denoising Low-rank patches PCA 


  1. 1.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Gilboa, G., Sochen, N., Zeevi, Y.Y.: Estimation of optimal PDE-based denoising in the SNR sense. IEEE Trans. Image Process. 15(8), 2269–2280 (2006)CrossRefGoogle Scholar
  3. 3.
    Donoho, D.L., Johnstone, J.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3), 425–455 (1994)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Khalil, H.H., Rahmat, R.O., Mahmoud, W.A.: Estimation of noise in gray-scale and colored images using median absolute deviation (MAD). In: 3rd International Conference on Geometric Modeling and Imaging, 2008. GMAI 2008. IEEE (2008)Google Scholar
  5. 5.
    Salmeri, M., Mencattini, A., Ricci, E., Salsano, A.: Noise estimation in digital images using fuzzy processing. In: Proceedings 2001 International Conference on Image Processing, 2001. IEEE (2001)Google Scholar
  6. 6.
    Immerkaer, J.: Fast noise variance estimation. Comput. Vis. Image Underst. 64(2), 300–302 (1996)CrossRefGoogle Scholar
  7. 7.
    Rank, K., Lendl, M., Unbehauen, R.: Estimation of image noise variance. IEE Proc. Vis. Image Signal Process. 146(2), 80–84 (1999)CrossRefGoogle Scholar
  8. 8.
    Bilcu, R., Vehvilainen, M.: New method for noise estimation in images. In: NSIP 2005. Abstracts. IEEE-Eurasip Nonlinear Signal and Image Processing, IEEE (2005)Google Scholar
  9. 9.
    Olsen, S.I.: Estimation of noise in images: an evaluation. CVGIP Gr. Models Image Process. 55(4), 319–323 (1993)CrossRefGoogle Scholar
  10. 10.
    Amer, A., Dubois, E.: Fast and reliable structure-oriented video noise estimation. IEEE Trans. Circuits Syst. Video Technol. 15(1), 113–118 (2005)CrossRefGoogle Scholar
  11. 11.
    Aja-Fernández, S., Vegas-Sánchez-Ferrero, G., Martín-Fernández, M., Alberola-López, C.: Automatic noise estimation in images using local statistics. Additive and multiplicative cases. Image Vis. Comput. 27(6), 756–770 (2009)CrossRefGoogle Scholar
  12. 12.
    Shin, D.-H., Park, R.H., Yang, S., Jung, J.H.: Block-based noise estimation using adaptive Gaussian filtering. IEEE Trans. Consum. Electron. 51(1), 218–226 (2005)CrossRefGoogle Scholar
  13. 13.
    Yang, S.-M., Tai, S.-C.: Fast and reliable image-noise estimation using a hybrid approach. J. Electron. Imaging 19(3), 033007-033007-15 (2010)Google Scholar
  14. 14.
    Liu, W.: Additive white Gaussian noise level estimation based on block SVD. In: IEEE Workshop on Electronics, Computer and Applications. IEEE (2014)Google Scholar
  15. 15.
    Liu, W., Lin, W.: Additive white Gaussian noise level estimation in SVD domain for images. IEEE Trans. Image Process. 22(3), 872–883 (2013)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Amer, A., Mitiche, A., Dubois, E.: Reliable and fast structure-oriented video noise estimation. In: Proceedings of the 2002 International Conference on Image Processing. IEEE (2002)Google Scholar
  17. 17.
    Corner, B., Narayanan, R., Reichenbach, S.E.: Noise estimation in remote sensing imagery using data masking. Int. J. Remote Sens. 24(4), 689–702 (2003)CrossRefGoogle Scholar
  18. 18.
    Pyatykh, S., Hesser, J., Zheng, L.: Image noise level estimation by principal component analysis. IEEE Trans. Image Process. 22(2), 687–699 (2013)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Khmag, A., Ramli, A.R., Al-haddad, S.A.R., Yusoff, S., Kamarudin, N.H.: Denoising of natural images through robust wavelet thresholding and genetic programming. Vis. Comput.,4, 1–14 (2016)Google Scholar
  20. 20.
    Zoran, D., Weiss, Y.: Scale invariance and noise in natural images. In: 2009 IEEE 12th International Conference on Computer Vision. IEEE (2009)Google Scholar
  21. 21.
    Ghazal, M., Amer, A.: Homogeneity localization using particle filters with application to noise estimation. IEEE Trans. Image Process. 20(7), 1788–1796 (2011)MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Jiang, P., Zhang, J.: Fast and reliable noise estimation algorithm based on statisti-cal hypothesis tests. In: Proceedings of IEEE International Conference on Visual Communications and Image Processing, San Diego, USA, pp. 27–30 (2012)Google Scholar
  23. 23.
    Bishop, C.M.: Pattern recognition. Mach. Learn. 128, 1–58 (2006)Google Scholar
  24. 24.
    Lee, J., Hoppel, K.: Noise modeling and estimation of remotely-sensed images. In: Geoscience and Remote Sensing Symposium, 1989. IGARSS’89. 12th Canadian Symposium on Remote Sensing., 1989 International. IEEE (1989)Google Scholar
  25. 25.
    Zhu, X., Milanfar, P.: Automatic parameter selection for denoising algorithms using a no-reference measure of image content. IEEE Trans. Image Process. 19(12), 3116–3132 (2010)MathSciNetCrossRefMATHGoogle Scholar
  26. 26.
    Petersen, K., Pedersen, M.: The matrix cookbook Technical University Denmark, Kongens Lyngby, Denmark. Technical Report, Version (2012)Google Scholar
  27. 27.
    Bigun, J., Granlund, G.H., Wiklund, J.: Multidimensional orientation estimation with applications to texture analysis and optical flow. IEEE Trans. Pattern Anal. Mach. Intell. 13(8), 775–790 (1991)CrossRefGoogle Scholar
  28. 28.
    Khmag, A., Ramli, A.R., bin Hashim, S.J., Al-Haddad, S.A.R.: Additive noise reduction in natural images using second-generation wavelet transform hidden Markov models. IEEJ Trans. Electr. Electron. Eng. 11(3), 339–347 (2016)Google Scholar
  29. 29.
    Ghazel, M., Freeman, G.H., Vrscay, E.R.: Fractal-wavelet image denoising revisited. IEEE Trans. Image Process. 15(9), 2669–2675 (2006)CrossRefGoogle Scholar
  30. 30.
    Khmag, A., Ramli, A.R., Al-Haddad, S.A.R., Hashim, S.J., Noh, Z.M., Najih, A.A.: Design of natural image denoising filter based on second-generation wavelet transformation and principal component analysis. J. Med. Imaging Health Inform. 5(6), 1261–1266 (2015)CrossRefGoogle Scholar
  31. 31.
    Database from University of Southern California. accessed 2016/9/26
  32. 32.
    Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: LIVE image quality assessment database release 2 (2005). Accessed 27 Mar 2017
  33. 33.
    Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M., Battisti, F.: TID2008-a database for evaluation of full-reference visual quality assessment metrics. Adv. Mod. Radioelectron. 10(4), 30–45 (2009)Google Scholar
  34. 34.
    Larson, E., Chandler, D.: Categorical subjective image quality CSIQ database (2009). Accessed 6 Nov 2016
  35. 35.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)CrossRefGoogle Scholar
  36. 36.
    Donoho, D.L., Johnstone, I.M.: Adapting to unknown smoothness via wavelet shrinkage. J Am Stat. Assoc. 90(432), 1200–1224 (1995)MathSciNetCrossRefMATHGoogle Scholar
  37. 37.
    Sendur, L., Selesnick, I.W.: Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Trans. Signal Process. 50(11), 2744–2756 (2002)CrossRefGoogle Scholar
  38. 38.
    Gonzales, R., Woods, R.: Digital Image Processing. Prentice-Hall, Inc, Upper Saddle River (2002)Google Scholar
  39. 39.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: BM3D image denoising with shape-adaptive principal component analysis. In: SPARS’09-Signal Processing with Adaptive Sparse Structured Representations (2009)Google Scholar
  40. 40.
    Goossens, B., Pizurica, A., Philips, W.: Removal of correlated noise by modeling the signal of interest in the wavelet domain. IEEE Trans. Image Process. 18(6), 1153–1165 (2009)MathSciNetCrossRefMATHGoogle Scholar
  41. 41.
    Ping, J., Jian-zhou, Z.: Fast and reliable noise level estimation based on local statistic. Pattern Recogn. Lett. 78, 8–13 (2016)CrossRefGoogle Scholar
  42. 42.
    Shao, L., Zhang, H., De Haan, G.: An overview and performance evaluation of classification-based least squares trained filters. IEEE Trans. Image Process. 17(10), 1772–1782 (2008)MathSciNetCrossRefMATHGoogle Scholar
  43. 43.
    Chen, G., Zhu, F., Ann Heng P.: An efficient statistical method for image noise level estimation. In: Proceedings of the IEEE International Conference on Computer Vision (2015)Google Scholar
  44. 44.
    Jain, P., Tyagi, V.: An adaptive edge-preserving image denoising technique using tetrolet transforms. Vis. Comput. 31(5), 657–674 (2014)CrossRefGoogle Scholar
  45. 45.
    Li, C., Bovik, A.C.: Three-component weighted structural similarity index. In: Farnand, S.P., Gaykema, F. (eds.) IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics. San Jose, CA (2009)Google Scholar
  46. 46.

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Faculty of EngineeringUniversity of ZawiaZawiyaLibya
  2. 2.Faculty of EngineeringUniversiti Putra Malaysia (UPM)SerdangMalaysia

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