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.
Similar content being viewed by others
References
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.
Russo, F., A method for estimation and filtering of Gaussian noise in images. IEEE Trans. Instrum. Meas. 52(4):1148–1154, 2003.
Vermaa A. and Shrey A., Image Denoising in Wavelet Domain, 1–10.
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.
Gonzalez, R. C., and Richard, E. W., Image processing. Digital image processing 2, 2007.
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.
Garnett, R. et al., A universal noise removal algorithm with an impulse detector. IEEE Trans. Image Process. 14:11, 2005.
Sairam, R. M., Sharma, S., and Gupta, K., Study of Denoising Method of Images-A Review, 2013.
Donoho, D. L., and Johnstone, I. M., Adapting to unknown smoothness via wavelet shrinkage. J. Am. Stat. Assoc. 90(432):1200–1224, 1995.
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.
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.
Bui, T. D., and Chen, G. Y., Translation invariant denoising using multiwavelets. IEEE Trans. Signal Process. 46(12):3414–3420, 1998.
Buades, A., Coll, B., and Morel, J. M., Non-local means denoising. Image Processing On Line:208–212, 2011.
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.
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.
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.
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.
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.
Elad, M., On the origin of the bilateral filter and ways to improve it. IEEE Trans. Image Process. 11(10):1141–1151, 2002.
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.
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.
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.
He, K., Sun, J., and Tang, X., Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6):1397–1409, 2013.
Chiu, L.-C., and Fuh, C.-S., A Robust Denoising Filter with Adaptive Edge Preservation. Berlin Heidelberg: Springer-Verlag, 2008, 923–926.
He K. and Sun, J., Fast guided filter. arXiv preprint arXiv:1505.00996, 2015.
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.
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.
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.
Sendur, L., and Selesnick, I. W., Bivariate Shrinkage with Local Variance Estimation. IEEE Signal Processing Letters 9(12):438–441, 2002.
Huerta, G., Bayesian wavelet shrinkage. Wiley Interdisciplinary Reviews: Computational Statistics 2(6):668–672, 2010.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
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.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the Topical Collection on Image & Signal Processing
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10916-019-1376-4