Abstract
A combination of the adaptive Kuwahara filter and BM3D algorithm is proposed in order to improve results with respect to both of these ingredients. The adaptive Kuwahara filter is used in pre-filtering stage for the BM3D algorithm. The combined filter has ability to filter Gaussian, impulsive, and mixed noise up to 20% of impulsive noise. The proposed filter is tested on various digital images and for different filtering quality measures and shows significant improvements with respect to both adaptive Kuwahara filter and BM3D.
Similar content being viewed by others
References
Smolka, B., Kusnik, D.: Robust local similarity filter for the reduction of mixed Gaussian and impulsive noise in color digital images. Signal Image Video Process. 9, 49–56 (2015)
Hu, H., de Haan, G.: Adding explicit content classification to nonlinear filters. Signal Image Video Process. 5, 291–305 (2011)
Djurović, I.: BM3D filter in salt and pepper noise removal. EURASIP J. Image Video Process. 2016, 13 (2016). doi:10.1186/s13640-016-0113-x
Papari, G., Petkov, N., Campisi, P.: Artistic edge and corner enhancing smoothing. IEEE Trans. Image Process. 16(10), 2449–2461 (2007)
Kuwahara, M., Hachimura, K., Eiho, S., Kinoshita, M.: Processing of RI-angiocardiographic images. In: Preston Jr, K., Onoe, M. (eds) Digital Processing of Biomedical Images. Plenum, NY, pp. 187–202 (1976)
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans. Image Proc. 16(8), 2080–2095 (2007)
Bartyzel, K.: Adaptive Kuwahara filter. Signal Image Video Process. doi:10.1007/s11760-015-0791-3
Sarjanoja, S.: BM3D image denoising using heterogeneous computing platforms, M.Sc. Thesis, University of Oulu (2015)
Lebrun, M.: An analysis and implementation of the BM3D image denoising method. Image Process. Line 2, 175–213 (2012). doi:10.5201/ipol.2012.l-bm3d
Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference Computer Vision and Pattern Recognition, vol. 2, pp. 60–65 (2005)
Sharifymoghaddam, M., Beheshti, S., Elahi, P., Hashemi, M.: Similarity validation based nonlocal means image denoising. IEEE Signal Process. Lett. 22(12), 2185–2188 (2015)
Chatterjee, P., Milanfar, P.: Is denoising dead? IEEE Trans. Image Process. 19(4), 895–911 (2010)
Zhang, J., Xiong, R., Zhao, C., Zhang, Y., Ma, S., Gao, W.: CONCOLOR: constrained non-covex low-rank model for image deblocking. IEEE Trans. Image Process. 25(3), 1246–1259 (2015)
Zang, Y., Huang, H., Zhang, L.: Efficient structure-aware image smoothing by local extrema on space-filling curve. IEEE Trans. Vizuelization Comput. Gr. 20(9), 1253–1265 (2014)
Tofighi, M., Kose, K., Cetin, A.E.: Denoising images corrupted by impulsive noise using projections onto the epigraph set of the total variation function (PES-TV). SIViP 9, 41–48 (2015). doi:10.1007/s11760-015-0827-8
Chou, Y.-L.: Statistical Analysis. Holt International (1975)
Huber, P.J., Ronchetti, E.M.: Robust statistics, 2nd edn. Wiley, Hoboken (2009). ISBN:978-0-470-12990-6
Astola, J., Kuosmanen, P.: Fundamentals of Nonlinear Digital Filtering. CRC Press Inc., Boca Raton (1997)
Foi, A., Katkovnik, V., Egiazarian, K.: Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images. IEEE Trans. Image Proc. 16(5), 1395–1411 (2007)
Malviya, S., Ahmia, H.: Image enhancement using improved mean filter at low and high noise density. Int. J. Emerg. Eng. Res. Technol. 2(3), 45–52 (2014)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Sattar, F., Floreby, L., Salomonsson, G., Lovstrom, B.: Image enhancement based on a nonlinear multiscale method. IEEE Trans. Image Process. 6(6), 888–895 (1997)
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, 30–45 (2009)
Acknowledgements
This research is supported in part by Ministry of Science of Montenegro through national project ‘Intelligent search techniques for parametric estimation in communication and power engineering,’ and EU FP7 Project Foremont.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Djurović, I. Combination of the adaptive Kuwahara and BM3D filters for filtering mixed Gaussian and impulsive noise. SIViP 11, 753–760 (2017). https://doi.org/10.1007/s11760-016-1019-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-016-1019-x