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Color Image Enhancement Using a Multiscale Morphological Approach

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Computer Science – CACIC 2018 (CACIC 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 995))

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Abstract

Color image enhancement has been widely applied in a variety of applications from different scientific areas. On the other hand, mathematical morphology is a theory that deals with describing shapes using sets and, therefore, it provides a number of useful tools for image enhancement. Despite its utility, one of the challenges of this theory, when applied to color images, is to determine an order between the components of the image. Color images are represented by multidimensional data structures, which implies that there is no natural order between their components. In this work we propose an image enhancement method for color images that uses the extension of the multiscale mathematical morphology with different color spaces and ordering methods. The experiments carried out show that the proposed method generates competitive results using different ordering methods in terms of both local and global contrast, as well as the color quality of the image.

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References

  1. Wang, Y.P., Wu, Q., Castleman, K.R., Xiong, Z.: Chromosome image enhancement using multiscale differential operators. IEEE Trans. Med. Imaging 22(5), 685–693 (2003)

    Article  Google Scholar 

  2. Mencattini, A., Salmeri, M., Lojacono, R., Frigerio, M., Caselli, F.: Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing. IEEE Trans. Instrum. Meas. 57(7), 1422–1430 (2008)

    Article  Google Scholar 

  3. Boccignone, G., Picariello, A.: Multiscale contrast enhancement of medical images. In: 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 4, pp. 2789–2792. IEEE (1997)

    Google Scholar 

  4. StojiC, T., Reljin, I., Reljin, B.: Local contrast enhancement in digital mammography by using mathematical morphology. In: International Symposium on Signals, Circuits and Systems, ISSCS 2005, vol. 2, pp. 609–612. IEEE (2005)

    Google Scholar 

  5. Angelelli, P., Nylund, K., Gilja, O.H., Hauser, H.: Interactive visual analysis of contrast-enhanced ultrasound data based on small neighborhood statistics. Comput. Graph. 35(2), 218–226 (2011)

    Article  Google Scholar 

  6. Yang, G.Z., Hansell, D.M.: CT image enhancement with wavelet analysis for the detection of small airways disease. IEEE Trans. Med. Imaging 16(6), 953–961 (1997)

    Article  Google Scholar 

  7. Truc, P.T., Khan, M.A., Lee, Y.K., Lee, S., Kim, T.S.: Vessel enhancement filter using directional filter bank. Comput. Vis. Image Underst. 113(1), 101–112 (2009)

    Article  Google Scholar 

  8. Yang, C., Lu, L., Lin, H., Guan, R., Shi, X., Liang, Y.: A fuzzy-statistics-based principal component analysis (FS-PCA) method for multispectral image enhancement and display. IEEE Trans. Geosci. Remote. Sens. 46(11), 3937–3947 (2008)

    Article  Google Scholar 

  9. Liao, B., Yin, P., Xiao, C.: Efficient image dehazing using boundary conditions and local contrast. Comput. Graph. 70, 242–250 (2018)

    Article  Google Scholar 

  10. Bai, X.: Microscopy mineral image enhancement through center operator construction. Appl. Opt. 54(15), 4678–4688 (2015)

    Article  Google Scholar 

  11. Wan, Y., Shi, D.: Joint exact histogram specification and image enhancement through the wavelet transform. IEEE Trans. Image Process. 16(9), 2245–2250 (2007)

    Article  MathSciNet  Google Scholar 

  12. Garg, R., Mittal, B., Garg, S.: Histogram equalization techniques for image enhancement. Int. J. Electron. Commun. Technol 2(1), 107–111 (2011)

    Google Scholar 

  13. Wong, C.Y., et al.: Histogram equalization and optimal profile compression based approach for colour image enhancement. J. Vis. Commun. Image Represent. 38, 802–813 (2016)

    Article  Google Scholar 

  14. Huang, J., Ma, Y., Zhang, Y., Fan, F.: Infrared image enhancement algorithm based on adaptive histogram segmentation. Appl. Opt. 56(35), 9686–9697 (2017)

    Article  Google Scholar 

  15. Choi, Y.S., Krishnapuram, R.: A robust approach to image enhancement based on fuzzy logic. IEEE Trans. Image Process. 6(6), 808–825 (1997)

    Article  Google Scholar 

  16. Greenspan, H., Anderson, C.H., Akber, S.: Image enhancement by nonlinear extrapolation in frequency space. IEEE Trans. Image Process. 9(6), 1035–1048 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  17. Agaian, S.S., Panetta, K., Grigoryan, A.M.: Transform-based image enhancement algorithms with performance measure. IEEE Trans. Image Process. 10(3), 367–382 (2001)

    Article  MATH  Google Scholar 

  18. Grigoryan, A.M., Agaian, S.S.: Transform-based image enhancement algorithms with performance measure. Adv. Imaging Electron. Phys. 130, 165–242 (2004)

    Article  Google Scholar 

  19. Ortiz Zamora, F.G.: Procesamiento morfológico de imágenes en color: aplicación a la reconstrucción geodésica (2002)

    Google Scholar 

  20. Joblove, G.H., Greenberg, D.: Color spaces for computer graphics. In: ACM SIGGRAPH Computer Graphics, vol. 12, pp. 20–25. ACM (1978)

    Google Scholar 

  21. Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, Inc., Cambridge (1983)

    Google Scholar 

  22. De, I., Chanda, B., Chattopadhyay, B.: Enhancing effective depth-of-field by image fusion using mathematical morphology. Image Vis. Comput. 24(12), 1278–1287 (2006)

    Article  Google Scholar 

  23. Bai, X., Zhou, F., Xue, B.: Image enhancement using multi scale image features extracted by top-hat transform. Opt. Laser Technol. 44(2), 328–336 (2012)

    Article  Google Scholar 

  24. Mukhopadhyay, S., Chanda, B.: A multiscale morphological approach to local contrast enhancement. Signal Process. 80(4), 685–696 (2000)

    Article  MATH  Google Scholar 

  25. Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-662-05088-0

    Book  MATH  Google Scholar 

  26. Román, J.C.M., Ayala, H.L., Noguera, J.L.V.: Image color contrast enhancement using multiscale morphology. In: 4th Conference of Computational Interdisciplinary Science (2016)

    Google Scholar 

  27. Bai, X., Zhou, F., Xue, B.: Noise-suppressed image enhancement using multiscale top-hat selection transform through region extraction. Appl. Opt. 51(3), 338–347 (2012)

    Article  Google Scholar 

  28. Liao, M., Zhao, Y.Q., Wang, X.H., Dai, P.S.: Retinal vessel enhancement based on multi-scale top-hat transformation and histogram fitting stretching. Opt. Laser Technol. 58, 56–62 (2014)

    Article  Google Scholar 

  29. Peng, B., Wang, Y., Yang, X.: A multiscale morphological approach to local contrast enhancement for ultrasound images. In: 2010 International Conference on Computational and Information Sciences, pp. 1142–1145. IEEE (2010)

    Google Scholar 

  30. Román, J.C.M., Ayala, H.L., Noguera, J.L.V.: Top-hat transform for enhancement of aerial thermal images. In: 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 277–284. IEEE (2017)

    Google Scholar 

  31. Zhao, J., Zhou, Q., Chen, Y., Feng, H., Xu, Z., Li, Q.: Fusion of visible and infrared images using saliency analysis and detail preserving based image decomposition. Infrared Phys. Technol. 56, 93–99 (2013)

    Article  Google Scholar 

  32. Bai, X., Zhou, F., Xue, B.: Fusion of infrared and visual images through region extraction by using multi scale center-surround top-hat transform. Opt. Express 19(9), 8444–8457 (2011)

    Article  Google Scholar 

  33. Ye, B., Peng, J.X.: Small target detection method based on morphology top-hat operator. J. Image Graph. 7(7), 638–642 (2002)

    Google Scholar 

  34. Aptoula, E., Lefèvre, S.: A comparative study on multivariate mathematical morphology. Pattern Recognit. 40(11), 2914–2929 (2007)

    Article  MATH  Google Scholar 

  35. Chanussot, J., Lambert, P.: Bit mixing paradigm for multivalued morphological filters. In: 1997 Sixth International Conference on Image Processing and Its Applications, vol. 2, pp. 804–808. IET (1997)

    Google Scholar 

  36. Noguera, J.L.V., Ayala, H.L., Schaerer, C.E., Facon, J.: A color morphological ordering method based on additive and subtractive spaces. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 674–678. IEEE (2014)

    Google Scholar 

  37. Cardozo, R., Méndez, Á., Legal Ayala, H., Vázquez Noguera, J.L.: Mejora de imágenes a color utilizando un enfoque morfológico multiescala. In: XXIV Congreso Argentino de Ciencias de la Computación (La Plata, 2018) (2018)

    Google Scholar 

  38. Tobar, M.C., Platero, C., González, P.M., Asensio, G.: Mathematical morphology in the HSI colour space. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4478, pp. 467–474. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72849-8_59

    Chapter  Google Scholar 

  39. Hanbury, A., Kandaswamy, U., Adjeroh, D.A.: Illumination-invariant morphological texture classification. In: Ronse, C., Najman, L., Decencière, E. (eds.) Mathematical Morphology: 40 Years On, vol. 30, pp. 377–386. Springer, Heidelberg (2005). https://doi.org/10.1007/1-4020-3443-1_34

    Chapter  Google Scholar 

  40. Mello Román, J.C., Vázquez Noguera, J.L., Legal-Ayala, H., Pinto-Roa, D.P., Gomez-Guerrero, S., García Torres, M.: Entropy and contrast enhancement of infrared thermal images using the multiscale top-hat transform. Entropy 21(3), 244 (2019)

    Article  Google Scholar 

  41. Chanussot, J., Lambert, P.: Total ordering based on space filling curves for multivalued morphology. Comput. Imaging Vis. 12, 51–58 (1998)

    MATH  Google Scholar 

  42. Arbelaez, P., Fowlkes, C., Martin, D.: The Berkeley segmentation dataset and benchmark (2007). http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds

  43. Gordon, R., Rangayyan, R.M.: Feature enhancement of film mammograms using fixed and adaptive neighborhoods. Appl. Opt. 23(4), 560–564 (1984)

    Article  Google Scholar 

  44. Hasler, D., Suesstrunk, S.E.: Measuring colorfulness in natural images. In: Human vision and electronic imaging VIII, vol. 5007, pp. 87–96. International Society for Optics and Photonics (2003)

    Google Scholar 

  45. Susstrunk, S.E., Winkler, S.: Color image quality on the internet. In: Internet Imaging V, vol. 5304, pp. 118–132. International Society for Optics and Photonics (2003)

    Google Scholar 

  46. Angulo, J., Serra, J.: Morphological coding of color images by vector connected filters. In: Proceedings of Seventh International Symposium on Signal Processing and Its Applications, vol. 1, pp. 69–72. IEEE (2003)

    Google Scholar 

  47. Ortiz, F., Torres, F., Gil, P.: Gaussian noise elimination in colour images by vector-connected filters. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 4, pp. 807–810. IEEE (2004)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Facultad Politécnica, Universidad Nacional de Asunción, San Lorenzo, Paraguay

    Raul Mendez, Rodolfo Cardozo, José Luis Vázquez Noguera, Horacio Legal-Ayala & Julio César Mello Román

  2. Universidad Autónoma de Asunción, Asunción, Paraguay

    Sebastian Grillo

  3. Division of Computer Science, Universidad Pablo de Olavide, 41013, Seville, Spain

    Miguel García-Torres

Authors
  1. Raul Mendez
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  2. Rodolfo Cardozo
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  3. José Luis Vázquez Noguera
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  4. Julio César Mello Román
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  5. Sebastian Grillo
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  6. Miguel García-Torres
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Corresponding author

Correspondence to José Luis Vázquez Noguera .

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Editors and Affiliations

  1. National University of La Plata, La Plata, Argentina

    Patricia Pesado

  2. National University of Buenos Aires Center, Buenos Aires, Argentina

    Claudio Aciti

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Mendez, R. et al. (2019). Color Image Enhancement Using a Multiscale Morphological Approach. In: Pesado, P., Aciti, C. (eds) Computer Science – CACIC 2018. CACIC 2018. Communications in Computer and Information Science, vol 995. Springer, Cham. https://doi.org/10.1007/978-3-030-20787-8_8

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  • DOI: https://doi.org/10.1007/978-3-030-20787-8_8

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