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Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.14 n.3 México Jan./Mar. 2011

 

Artículos

 

Contrast Enhancement Based on a Morphological Rational Multiscale Algorithm

 

Mejora de contraste basada en un algoritmo morfológico racional multiescala

 

Hayde Peregrina Barreto1 and Iván R. Terol Villalobos2

 

1 Facultad de Ingeniería, Universidad Autónoma de Querétaro–Campus San Juan del Río Querétaro, México. Email: hperegrina@ieee.org

2 Centro de Investigación y Desarrollo Tecnológico en Electroquímica (CIDETEQ) Querétaro, México. Email: famter@ciateq.net.mx

 

Article received on July 27, 2009
Accepted on January 06, 2010

 

Abstract

Contrast enhancement is an important task in image processing and it is commonly used as a preprocessing step in order to improve the results for other tasks such as segmentation. However, not only do some methods for contrast improvement have good performance working on low contrast regions, but they also affect good contrast regions; owing to the fact that some elements could be vanished, representing a loss of information. A method focused on images with different luminance conditions is introduced in the present work. The proposed method is based on morphological filters by reconstruction and rational operations, which together, allow a uniform contrast enhancement. Furthermore, due to the properties of these morphological transformations, the creation of new elements on image is avoided. The processing was made on luminance values in the u'v'Y' color space, which permits to keep the chrominance and to avoid the creation of new colors. As a result of the previous considerations, the proposed method keeps the natural color appearance of the image.

Keywords: Contrast enhancement, Rational operations, Morphological filters, Mathematical morphology.

 

Resumen

La mejora del contraste es una tarea importante en procesamiento de imágenes y a menudo es usada como paso de pre–procesamiento a fin de mejorar los resultados de procesos como segmentación. Algunos métodos para mejora de contraste tienen un buen desempeño trabajando en regiones con poco contraste pero también afectan las regiones con suficiente contraste; este es un efecto no deseado debido a que algunos elementos de la imagen pueden ser eliminados lo cual representa una pérdida de información. En este trabajo se presenta una mejora de contraste enfocada a imágenes que tienen diferente luminancia sobre la misma escena. El método propuesto está basado en filtros morfológicos por reconstrucción y operaciones racionales, que en conjunto permiten una mejora de contraste uniforme. Además, debido a las propiedades de estas transformaciones morfológicas se evita la creación de nuevos elementos. El procesamiento trabaja sobre los valores de luminancia en el espacio de color u'v'Y', lo cual permite mantener el croma y evitar la creación de nuevos colores. Como resultado de las consideraciones mencionadas, este método provee una mejora de contraste uniforme y mantiene la apariencia natural de la imagen.

Palabras clave: Mejora de contraste, Operaciones racionales, Filtros morfológicos, Morfología matemática.

 

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Acknowledgements

We would like to thank the anonymous reviewers for their valuable comments. The author Hayde Peregrina–Barreto thanks the government agency CONACyT for the financial support scholarship 206082. This work was funded by the government agency CONACyT (58367), Mexico.

 

Referencias

1. Barnard, K. & Funt, B. (1999). Investigations into multi–scale Retinex. Colour Imaging: Vision and Technology (9–17). New York: Wiley.         [ Links ]

2. Dornaika, F. & Zhang, H. (2000). Granulometry using mathematical morphology and motion.IAPR Workshop on Machine Vision Applications, Tokio, Japan, 51–54.         [ Links ]

3. Espino–Gudiño, M., Santillan, I.& Terol–Villalobos, I. R. (2007). Morphological multiscale contrast approach for gray and color images consistent with human visual perception, Optical Engineering, 46(6), 1–14.         [ Links ]

4. Fairchild, M. D. (2005). Color appearance models (2nd. Ed.), Hoboken, NJ: Wiley.         [ Links ]

5. González, R. C. & Woods, R. E. (1992). Digital image processing, Reading, Mass: Addison–Wesley        [ Links ]

6. Hunt, R. W. G. (2001).Measuring color(3rd. Ed.),London: Fountain Press.         [ Links ]

7. Kogan, R. G., Agaian, S. & Lentz, K. P. (1998). Visualization using rational morphology and magnitude reduction.SPIE Conference Visual Information Processing VII, Orlando, Florida, USA, 153–163.         [ Links ]

8. Kramer, H. P. & Bruckner, J. B. (1975). Iteration of nonlinear transformation for enhancement of digital image.Pattern Recognition, 7(1–2), 53–58.         [ Links ]

9. Land, E. (1986). Recent advances in retinex theory. Vision Research, 26(1), 7–21.         [ Links ]

10. Land, E. & McCann J. J. (1971).Lightness and retinex theory.Journal of the Optical Society of America, 61(1), 1–11.         [ Links ]

11. Lantuéjoul, C. & Maisonneuve, F. (1984). Geodesic methods in quantitative image analysis.Pattern Recognition, 17(2), 177–187.         [ Links ]

12. Levkowitz, H. & Herman, G. T. (1993). GLHS: A generalized lightness, hue and saturation color models. CVGIP: Graphical Models and Image Processing, 55(4), 271–285.         [ Links ]

13. Lucchese, L. & Mitra, S. K. (2004). A new class of chromatic filters for color image processing. Theory and applications.IEEE Transactions on Image Processing, 13(4), 534–548.         [ Links ]

14. Matheron, G. (1967). Eléments pour unethéorie des Milieuxporeux, Paris: Masson.         [ Links ]

15. Matheron, G. (1975). Random sets and integral geometry, New York: John Wiley and Sons.         [ Links ]

16. Mendiola–Santibañez, J. D. & Terol–Vilallobos, I. R.(2002). Mapeos de contraste morfológicos sobre particiones basados en la noción de zona plana. Computación y Sistemas, 6(1), 25–37.         [ Links ]

17. Mendiola–Santibañez, J. D. & Terol–Vilallobos, I. R. (2005). Quantifying contrast methods through morphological gradient.Computación y Sistemas, 8(4), 317–333.         [ Links ]

18. Meyer, F. & Serra, J. (1989). Contrast and activity lattice. Signal Processing, 16(4), 303–317.         [ Links ]

19. Mukhopadhyay, S. & Chanda, B. (2000). A Multiscale morphological approach to local contrast enhancement. Signal Processing, 80(4), 685–696.         [ Links ]

20. Pei, S. C., Zeng, Y. C. & Chang, C. H. (2004). Virtual restoration of ancient Chinese paintings using color contrast enhancement and lacuna texture synthesis. IEEE Transactions on Image Processing, 13(3): 416–429.         [ Links ]

21. Rizzi, A.,Gatta C., & Marini D. (2004). From Retinex to automatic color equalization: issues in developing a new algorithm for unsupervised color equalization. Journal of Electronic Imaging, 13(1): 75–84.         [ Links ]

22. Rahman, Z.,Jobson, D. J. & Woodell, G. A. (2004). Retinex processing for automatic image enhancement", Journal of Electronic Imaging, 13(1): 100–110.         [ Links ]

23. Serra, J. (1982). Image analysis and mathematical morphology, New York: Academic Press.         [ Links ]

24. Serra, J. (1988). Toggle Mappings (Technical report N–18/88/MM). Fontainebleau, France: Centre de MorphologieMatematique.         [ Links ]

25. Smith, A. R. (1978). Color gamut transform pairs. ACM SIGGRAPH Compuer Graphics, 12(3), 12–19.         [ Links ]

26. Soille, P. (2003). Morphological image analysis: principles and applications. New York: Springer–Verlag.         [ Links ]

27. Süsstrunk, S., Holm, J. & Finlayson, G. D. (2001). Chromatic adaptation performance of different rgb sensors. IS & T/SPIE Electronic Imaging, California, USA, 4300, 172–183.         [ Links ]

28. Terol–Villalobos, I. R. (1995). Morphological slope filters. Intelligent Robots and ComputerVision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, Philadelphia, USA, 2588, 712–722.         [ Links ]

29. Terol–Villalobos, I. R. (1996). Non–increasing filters using morphological gradient criteria. Optical Engineering, 35(11), 3172–3182.         [ Links ]

30. Toet, A. (1990). Hierarchical image fusion. Machine Vision and Applications, 3(1), 1–11.         [ Links ]

31. Toet, A. (1992). Multi–scale contrast enhancement with applications to image fusion.Optical Engineering, 31(5): 1026–1031.         [ Links ]

32. Vincent, L. (1997). Current topics in applied morphological image analysis. In W.S. Kendall, O.E. Barndorff–Nielsen, and M.C. van Lieshout (Eds.), Current Trends in Stochastics Geometry and its Applications (3–91). London: Chapman & Hall.         [ Links ]

33. Vincent L. (2000). Granulometries and opening trees. Fundamenta Informaticae, 41(1–2): 57–90.         [ Links ]

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