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

  • Image & Signal Processing
  • Published:
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Abstract

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.

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References

  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.

    Article  Google Scholar 

  2. Russo, F., A method for estimation and filtering of Gaussian noise in images. IEEE Trans. Instrum. Meas. 52(4):1148–1154, 2003.

    Article  Google Scholar 

  3. Vermaa A. and Shrey A., Image Denoising in Wavelet Domain, 1–10.

  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. Gonzalez, R. C., and Richard, E. W., Image processing. Digital image processing 2, 2007.

  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.

    Article  Google Scholar 

  7. Garnett, R. et al., A universal noise removal algorithm with an impulse detector. IEEE Trans. Image Process. 14:11, 2005.

    Article  Google Scholar 

  8. Sairam, R. M., Sharma, S., and Gupta, K., Study of Denoising Method of Images-A Review, 2013.

  9. Donoho, D. L., and Johnstone, I. M., Adapting to unknown smoothness via wavelet shrinkage. J. Am. Stat. Assoc. 90(432):1200–1224, 1995.

    Article  Google Scholar 

  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. 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. Bui, T. D., and Chen, G. Y., Translation invariant denoising using multiwavelets. IEEE Trans. Signal Process. 46(12):3414–3420, 1998.

    Article  Google Scholar 

  13. Buades, A., Coll, B., and Morel, J. M., Non-local means denoising. Image Processing On Line:208–212, 2011.

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  19. Elad, M., On the origin of the bilateral filter and ways to improve it. IEEE Trans. Image Process. 11(10):1141–1151, 2002.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

  23. He, K., Sun, J., and Tang, X., Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6):1397–1409, 2013.

    Article  Google Scholar 

  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. He K. and Sun, J., Fast guided filter. arXiv preprint arXiv:1505.00996, 2015.

  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.

    Article  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  29. Sendur, L., and Selesnick, I. W., Bivariate Shrinkage with Local Variance Estimation. IEEE Signal Processing Letters 9(12):438–441, 2002.

    Article  Google Scholar 

  30. Huerta, G., Bayesian wavelet shrinkage. Wiley Interdisciplinary Reviews: Computational Statistics 2(6):668–672, 2010.

    Article  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

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Correspondence to S. Shaik Majeeth.

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Majeeth, S.S., Babu, C.N.K. Gaussian Noise Removal in an Image using Fast Guided Filter and its Method Noise Thresholding in Medical Healthcare Application. J Med Syst 43, 280 (2019). https://doi.org/10.1007/s10916-019-1376-4

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