Abstract
In this paper, to reduce impulsive noise in color images we propose an extension of the Median Redescending M-Estimator. For that purpose, a multitasking approach was developed such as a multi-core processing in order to reduce in parallel the noise on R, G and B color channels. With this paradigm, an acceleration up to three times can be guaranteed compared to the sequential paradigm, while having the ability to reduce corrupted data up to densities of \(80\%\) of fixed-value and \(40\%\) of random-value impulsive noises, guaranteeing the preservation of edges. The effectiveness of our proposal is verified by quantitative and qualitative results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Roy, A., Manam, L., Laskar, R.H.: Removal of ‘salt & pepper’noise from color images using adaptive fuzzy technique based on histogram estimation. Multimedia Tools Appl. 1–23 (2020)
Gökcen, A., Kalyoncu, C.: Real-time impulse noise removal. J. Real-Time Image Proc. 17(3), 459–469 (2018). https://doi.org/10.1007/s11554-018-0791-y
Karthik, B., Krishna Kumar, T., Vijayaragavan, S.P., Sriram, M.: Removal of high density salt and pepper noise in color image through modified cascaded filter. J. Ambient. Intell. Humaniz. Comput. 12(3), 3901–3908 (2020). https://doi.org/10.1007/s12652-020-01737-1
Singh, A., Sethi, G., Kalra, G.: Spatially adaptive image denoising via enhanced noise detection method for grayscale and color images. IEEE Access 8, 112985–113002 (2020)
Ma, C., Lv, X., Ao, J.: Difference based median filter for removal of random value impulse noise in images. Multimedia Tools Appl. 78(1), 1131–1148 (2018). https://doi.org/10.1007/s11042-018-6442-2
Malinski, L., Smolka, B.: Fast adaptive switching technique of impulsive noise removal in color images. J. Real-Time Image Proc. 16(4), 1077–1098 (2016). https://doi.org/10.1007/s11554-016-0599-6
Malinski, L., Smolka, B.: Self-tuning fast adaptive algorithm for impulsive noise suppression in color images. J. Real-Time Image Proc. 17(4), 1067–1087 (2019). https://doi.org/10.1007/s11554-019-00853-2
Roy, A., Laskar, R.H.: Fuzzy SVM based fuzzy adaptive filter for denoising impulse noise from color images. Multimedia Tools Appl. 78, 1785–1804 (2019)
Mújica-Vargas, D., Gallegos-Funes, F.J., de Jesús Rubio, J., Pacheco, J.: Impulsive noise filtering using a median redescending m-estimator. Intell. Data Anal. 21, 739–754 (2017)
Mújica-Vargas, D., de Jesús Rubio, J., Kinani, J.M.V., Gallegos-Funes, F.J.: An efficient nonlinear approach for removing fixed-value impulse noise from grayscale images. J. Real-Time Image Proc. 14(3), 617–633 (2017). https://doi.org/10.1007/s11554-017-0746-8
Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, vol. 2, pp. 1398–1402. IEEE (2003)
Acknowledgements
The authors thank to CONACYT, as well as TecNM-CENIDET for their financial support through the project “Controlador Difuso para ajuste de coeficientes de rigidez de un modelo deformable para simulación en tiempo real de los tejidos del hígado humano”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Mújica-Vargas, D., Rendón-Castro, A., Matuz-Cruz, M., Garcia-Aquino, C. (2021). Multi-core Median Redescending M-Estimator for Impulsive Denoising in Color Images. In: Roman-Rangel, E., Kuri-Morales, Á.F., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2021. Lecture Notes in Computer Science(), vol 12725. Springer, Cham. https://doi.org/10.1007/978-3-030-77004-4_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-77004-4_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77003-7
Online ISBN: 978-3-030-77004-4
eBook Packages: Computer ScienceComputer Science (R0)
