Journal of Medical Systems

, 43:280 | Cite as

Gaussian Noise Removal in an Image using Fast Guided Filter and its Method Noise Thresholding in Medical Healthcare Application

  • S. Shaik MajeethEmail author
  • C. Nelson Kennedy Babu
Image & Signal Processing
Part of the following topical collections:
  1. Wearable Computing Techniques for Smart Health


A new denoising algorithm using Fast Guided Filter and Discrete Wavelet Transform is proposed to remove Gaussian noise in an image. The Fast Guided Filter removes some part of the details in addition to noise. These details are estimated accurately and combined with the filtered image to get back the final denoised image. The proposed algorithm is compared with other existing filtering techniques such as Wiener filter, Non Local means filter and bilateral filter and it is observed that the performance of this algorithm is superior compared to the above mentioned Gaussian noise removal techniques. The resultant image obtained from this method is very good both from subjective and objective point of view. This algorithm has less computational complexity and preserves edges and other detail information in an image.


Fast guided filter Method noise Wavelet thresholding 


Compliance with ethical Standards

Conflict of interest

This paper has not communicated anywhere till this moment, now only it is communicated to your esteemed journal for the publication with the knowledge of all co-authors.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    Garnett, R., Huegerich, T., Chui, C., and He, W., A universal noise removal algorithm with an impulse detector. IEEE Trans. Image Process. 14(11):1747–1754, 2005.CrossRefGoogle Scholar
  2. 2.
    Russo, F., A method for estimation and filtering of Gaussian noise in images. IEEE Trans. Instrum. Meas. 52(4):1148–1154, 2003.CrossRefGoogle Scholar
  3. 3.
    Vermaa A. and Shrey A., Image Denoising in Wavelet Domain, 1–10.Google Scholar
  4. 4.
    Sairam, R. M., Sharma, S., and Gupta, K., Study of Denoising Method of Images-A Review. Journal of Engineering Science and Technology Review 8(5):41–48, 2013.Google Scholar
  5. 5.
    Gonzalez, R. C., and Richard, E. W., Image processing. Digital image processing 2, 2007.Google Scholar
  6. 6.
    Xiong, B., and Yin, Z., A universal denoising framework with a new impulse detector and nonlocal means. IEEE Trans. Image Process. 21(4):1663–1675, 2012.CrossRefGoogle Scholar
  7. 7.
    Garnett, R. et al., A universal noise removal algorithm with an impulse detector. IEEE Trans. Image Process. 14:11, 2005.CrossRefGoogle Scholar
  8. 8.
    Sairam, R. M., Sharma, S., and Gupta, K., Study of Denoising Method of Images-A Review, 2013.Google Scholar
  9. 9.
    Donoho, D. L., and Johnstone, I. M., Adapting to unknown smoothness via wavelet shrinkage. J. Am. Stat. Assoc. 90(432):1200–1224, 1995.CrossRefGoogle Scholar
  10. 10.
    Steidl, G., and Weickert, J., Relations between soft wavelet shrinkage and total variation denoising. In: Van Gool, L. (Ed.), Pattern Recognition, Lecture Notes in Computer Science, vol. 2449. Berlin: Springer, 2002, 198–205.Google Scholar
  11. 11.
    Steidl, G., Weickert, J., Brox, T., Mrázek, P., and Welk, M., On the equivalence of soft wavelet shrinkage, total variation diffusion, total variation regularization, and SIDEs, Technical Report, Series SPP-1114. Germany: Department of Mathematics, University of Bremen, 2003.Google Scholar
  12. 12.
    Bui, T. D., and Chen, G. Y., Translation invariant denoising using multiwavelets. IEEE Trans. Signal Process. 46(12):3414–3420, 1998.CrossRefGoogle Scholar
  13. 13.
    Buades, A., Coll, B., and Morel, J. M., Non-local means denoising. Image Processing On Line:208–212, 2011.Google Scholar
  14. 14.
    Raghuvanshi, D., Singh, H., Jain, P., and Mathur, M., Comparative Study of Non-Local Means and Fast Non–Local Means Algorithm for Image Denoising. International Journal of Advances in Engineering & Technology 4(2):247–254, 2012.Google Scholar
  15. 15.
    Zhang, L., Dong, W., Zhang, D., and Shi, G., Two-stage image denoising by principal component analysis with local pixel grouping. Pattern Recogn. 43:1531–1549, April 2010.CrossRefGoogle Scholar
  16. 16.
    Dabov, A., Foi, V., Katkovnik, K. E., and Member, S., Image denoising by sparse 3d transform domain collaborative filtering. IEEE Trans. Image Process. 16, 2007, 2007.Google Scholar
  17. 17.
    Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K., Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8):2080–2095, 2007.CrossRefGoogle Scholar
  18. 18.
    Zhang, L., Dong, W., Zhang, D., and Shi, G., Two-stage image denoising by principal component analysis with local pixel grouping. Pattern Recogn. 43:1531–1549, 2010.CrossRefGoogle Scholar
  19. 19.
    Elad, M., On the origin of the bilateral filter and ways to improve it. IEEE Trans. Image Process. 11(10):1141–1151, 2002.CrossRefGoogle Scholar
  20. 20.
    Kumar, B. S., Image denoising based on non-local means filter and its method noise thresholding. Signal Image and Video Processing 7(6):1211–1227, 2013.CrossRefGoogle Scholar
  21. 21.
    Varsha, A., and Basu P., An improved dual tree complex wavelet transform based image denoising using GCV thresholding., Computational Systems and Communications (ICCSC), First International Conference on IEEE, 2014.Google Scholar
  22. 22.
    Pham, C. C., Ha, U., and Jeon, J. W., Adaptive guided image filtering for sharpness enhancement and noise reduction. Proceedings of Advances in Image and Video technology, Lecture Notes in Computer Science:323–334, 2012.Google Scholar
  23. 23.
    He, K., Sun, J., and Tang, X., Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6):1397–1409, 2013.CrossRefGoogle Scholar
  24. 24.
    Chiu, L.-C., and Fuh, C.-S., A Robust Denoising Filter with Adaptive Edge Preservation. Berlin Heidelberg: Springer-Verlag, 2008, 923–926.Google Scholar
  25. 25.
    He K. and Sun, J., Fast guided filter. arXiv preprint arXiv:1505.00996, 2015.Google Scholar
  26. 26.
    Kao, C.-C., Lai, J.-H., and Chien, S.-Y., VLSI architecture design of Guided Filter for 30 Frames/s full HD video. IEEE Transactions on Circuits and Systems for Video Technology 24(3), 2014.Google Scholar
  27. 27.
    Suresh, K. V., An improved image denoising using wavelet transform, Trends in Automation, Communications and Computing Technology (I-TACT-15), International Conference on (IEEE) Vol. 1. 2015.Google Scholar
  28. 28.
    Sendur, L., and Selesnick, I. W., Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Trans. Signal Process. 50(11):2744–2756, 2002.CrossRefGoogle Scholar
  29. 29.
    Sendur, L., and Selesnick, I. W., Bivariate Shrinkage with Local Variance Estimation. IEEE Signal Processing Letters 9(12):438–441, 2002.CrossRefGoogle Scholar
  30. 30.
    Huerta, G., Bayesian wavelet shrinkage. Wiley Interdisciplinary Reviews: Computational Statistics 2(6):668–672, 2010.CrossRefGoogle Scholar
  31. 31.
    Chang, S. G., Yu, B., and Vetterli, M., Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. 9(9):1532–1546, 2000.CrossRefGoogle Scholar
  32. 32.
    Al-Najjar, Y. A. Y., and Soong, D. D. C., Comparison of Image Quality Assessment: PSNR, HVS, SSIM, UIQI. Int. J. Sci. Eng. Res. 3(8), 2012.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Saveetha Engineering CollegeChennaiIndia
  2. 2.SMK Fomra Institute of TechnologyChennaiIndia

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