Artículos

 

Morphological Filtering Algorithm for Restoring Images Contaminated by Impulse Noise

 

Jorge Domingo Mendiola-Santibañez^{1, 3}, Miguel Octavio Arias-Estrada², Israel Marcos Santillán-Méndez³, Juvenal Rodríguez-Reséndiz³, Martín Gallegos-Duarte⁴, Domingo José Gómez-Meléndez⁵, Ivan Ramón Terol-Villalobos¹

 

¹ Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Querétaro, México. mendijor@uaq.mx, iterol@cideteq.mx

² Instituto Nacional de Astrofísica, (Óptica y Electrónica, Coordinación de Ciencias Computacionales, Puebla, México. ariasmo@inaoep.mx

³ Universidad Autónoma de Querétaro, Facultad de Ingeniería, Querétaro, México. santilis@gmail.com, juvenal@ieee.org

⁴ Universidad Autónoma de Querétaro, Doctorado de la Facultad de Medicina, Querétaro, México. martin_oso@hotmail.com

⁵ Universidad Politécnica de Querétaro, Querétaro, México. domag5@hotmail.com

Corresponding author is Jorge Domingo Mendiola-Santibañez.

 

Article received on 22/11/2013.
Accepted on 09/03/2015.

 

Abstract

In this paper a methodology to restore gray scale images with pixels polluted by random impulsive noise is presented. Noise is discovered using a criterion based on the white top-hat by reconstruction. Pixels detected as corrupted are restored using an iterative morphological algorithm built with extensive and antiextensive morphological transformations. The proposal is compared with the rank ordered mean filter (ROM) and other morphological transformations reported in the current literature.

Keywords: Noise detection, morphological pixel restoration, transformations by reconstruction.

 

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Acknowledgements

The authors wish to thank the Mario Moreno Reyes foundation for the financial support. Ivan R. Terol-Villalobos would like to thank Diego Rodrigo and Darío T.G. for their great encouragement. This work was funded by the government agency CONACyT (133697), Mexico.

 

References

1. Abreu, E., Lightstone, M., Mitra, S., & Arakawa, K. (1996). A new efficient approach for the removal of impulse noise from highly corrupted images. Image Processing, IEEE Transactions on, Vol. 5, No. 6, pp. 1012-1025.         [ Links ]

2. Astola, J. & Kousmanen, P. (1997). Fundamentals of Nonlinear Digital Filtering. Boca Raton, CRC.         [ Links ]

3. Chan, R., Ho, C.-W., Leung, C.-Y., & Nikolova, M. (2005). Minimization of detail-preserving regularization functional by newton's method with continuation. Image Processing, 2005. ICIP 2005. IEEE International Conference on, volume 1, pp. I-125-8.         [ Links ]

4. Chen, T. &Wu, H. R. (2001). Space variant median filters for the restoration of impulse noise corrupted images. Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on, Vol. 48, No. 8, pp. 784-789.         [ Links ]

5. Emre Celebi, M. (2009). Real-time implementation of order-statistics-based directional filters. Image Processing, IET, Vol. 3, No. 1, pp. 1-9.         [ Links ]

6. Heijmans, H. J. A. M. (1994). Morphological image operators. Advances in electronics and electron physics, supplement. Academic Press, Boston, MA.         [ Links ]

7. Hwang, H. & Haddad, R. (1995). Adaptive median filters: new algorithms and results. Image Processing, IEEE Transactions on, Vol. 4, No. 4, pp. 499-502.         [ Links ]

8. Lerallut, R., Decencière, E., & Meyer, F. (2007). Image filtering using morphological amoebas. Image and Vision Computing, Vol. 25, No. 4, pp. 395-404.         [ Links ]

9. Mendiola-Santibanez, J. D. & Terol-Villalobos, I. R. (2014). Filtering of mixed gaussian and impulsive noise using morphological contrast detectors. IET Image Processing, Vol. 8, pp. 131-141.         [ Links ]

10. Mendiola-Santibanez, J. D., Terol-Villalobos, I. R., Jimenez-Sanchez, A. R., Gallegos-Duarte, M., Rodriguez-Resendiz, J., & Santillan, I. (2011). Application of morphological connected openings and levelings on magnetic resonance images of the brain. International Journal of Imaging Systems and Technology, Vol. 21, No. 4, pp. 336-348.         [ Links ]

11. Nikolova, M. (2004). A variational approach to remove outliers and impulse noise. Journal of Mathematical Imaging and Vision, Vol. 20, No. 1-2, pp. 99-120.         [ Links ]

12. Sasaki, H. (2009). A new flat pattern oriented order statistic filter for impulse noise reduction from highly corrupted images. Proceedings of the IAPR Conference on Machine Vision Applications (IAPR MVA 2009), Keio University, Yokohama, Japan, May 20-22, 2009, pp. 358-361.         [ Links ]

13. Serra, J. (2012). Tutorial on connective morphology. Selected Topics in Signal Processing, IEEE Journal of, Vol. 6, No. 7, pp. 739-752.         [ Links ]

14. Shannon, C. E. (2001). A mathematical theory of communication. SIGMOBILE Mob. Comput. Commun. Rev., Vol. 5, No. 1, pp. 3-55.         [ Links ]

15. Terol-Villalobos, I. R. (2004). Morphological connected contrast mappings based on top-hat criteria: a multiscale contrast approach. Optical Engineering, Vol. 43, No. 7, pp. 1577-1595.         [ Links ]

16. Vincent, L. (1993). Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. Image Processing, IEEE Transactions on, Vol. 2, No. 2, pp. 176-201.         [ Links ]

17. Vincent, L. (1994). Morphological area openings and closings for grey-scale images. In O, Y.-L., Toet, A., Foster, D., Heijmans, H., & Meer, P., editors, Shape in Picture, volume 126 of NATO ASI Series. Springer Berlin Heidelberg, pp. 197-208.         [ Links ]

18. Wang, Z., Bovik, A., Sheikh, H., & Simoncelli, E. (2004). Image quality assessment: from error visibility to structural similarity. Image Processing, IEEE Transactions on, Vol. 13, No. 4, pp. 600-612.         [ Links ]