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Logarithmic Mathematical Morphology: A New Framework Adaptive to Illumination Changes

  • Guillaume NoyelEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

A new set of mathematical morphology (MM) operators adaptive to illumination changes caused by variation of exposure time or light intensity is defined thanks to the Logarithmic Image Processing (LIP) model. This model based on the physics of acquisition is consistent with human vision. The fundamental operators, the logarithmic-dilation and the logarithmic-erosion, are defined with the LIP-addition of a structuring function. The combination of these two adjunct operators gives morphological filters, namely the logarithmic-opening and closing, useful for pattern recognition. The mathematical relation existing between “classical” dilation and erosion and their logarithmic-versions is established facilitating their implementation. Results on simulated and real images show that logarithmic-MM is more efficient on low-contrasted information than “classical” MM.

Keywords

Mathematical morphology Contrast variations Illumination changes Logarithmic Image Processing Pattern recognition 

References

  1. 1.
    Banon, G.J.F., Barrera, J.: Decomposition of mappings between complete lattices by mathematical morphology, part i. general lattices. Sig. Process. 30(3), 299–327 (1993).  https://doi.org/10.1016/0165-1684(93)90015-3
  2. 2.
    Brailean, J., et al.: Evaluating the EM algorithm for image processing using a human visual fidelity criterion. In: ICASSP-91, vol. 4, pp. 2957–2960, April 1991.  https://doi.org/10.1109/ICASSP.1991.151023
  3. 3.
    Heijmans, H.: Morphological Image Operators. Advances in Electronics and Electron Physics: Supplement, vol. 25. Academic Press, New York (1994)zbMATHGoogle Scholar
  4. 4.
    Jourlin, M., Pinoli, J.: Logarithmic image processing: the mathematical and physical framework for the representation and processing of transmitted images. In: Hawkes, P.W. (ed.) Advances in Imaging and Electron Physics, vol. 115, pp. 129–196. Elsevier (2001).  https://doi.org/10.1016/S1076-5670(01)80095-1
  5. 5.
    Jourlin, M.: Logarithmic Image Processing: Theory and Applications. Advances in Imaging and Electron Physics, vol. 195. Elsevier Science, New York (2016)Google Scholar
  6. 6.
    Jourlin, M., Pinoli, J.C.: Image dynamic range enhancement and stabilization in the context of the logarithmic image processing model. Sig. Process. 41(2), 225–237 (1995).  https://doi.org/10.1016/0165-1684(94)00102-6CrossRefzbMATHGoogle Scholar
  7. 7.
    Lai, Z.R., et al.: Multilayer surface albedo for face recognition with reference images in bad lighting conditions. IEEE Trans. Image Process. 23(11), 4709–4723 (2014).  https://doi.org/10.1109/TIP.2014.2356292MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Matheron, G.: Eléments pour une théorie des milieux poreux. Masson, Paris (1967)Google Scholar
  9. 9.
    Meylan, L., Susstrunk, S.: High dynamic range image rendering with a retinex-based adaptive filter. IEEE Trans. Image Process. 15(9), 2820–2830 (2006).  https://doi.org/10.1109/TIP.2006.877312CrossRefGoogle Scholar
  10. 10.
    Najman, L., Talbot, H.: Mathematical Morphology. ISTE , Wiley (2013)Google Scholar
  11. 11.
    Navarro, L., Courbebaisse, G., Jourlin, M.: Chapter two - logarithmic wavelets. In: Hawkes, P.W. (ed.) Advances in Imaging and Electron Physics, vol. 183, pp. 41–98. Elsevier (2014).  https://doi.org/10.1016/B978-0-12-800265-0.00002-3
  12. 12.
    Navarro, L., Deng, G., Courbebaisse, G.: The symmetric logarithmic image processing model. Digit. Sig. Process. 23(5), 1337–1343 (2013).  https://doi.org/10.1016/j.dsp.2013.07.001MathSciNetCrossRefGoogle Scholar
  13. 13.
    Noyel, G., et al.: Superimposition of eye fundus images for longitudinal analysis from large public health databases. Biomed. Phys. Eng. Express 3(4), 045015 (2017).  https://doi.org/10.1088/2057-1976/aa7d16CrossRefGoogle Scholar
  14. 14.
    Oppenheim, A.V., et al.: Nonlinear filtering of multiplied and convolved signals. Proc. IEEE 56(8), 1264–1291 (1968).  https://doi.org/10.1109/PROC.1968.6570CrossRefGoogle Scholar
  15. 15.
    Peng, Y.T., Cosman, P.C.: Underwater image restoration based on image blurriness and light absorption. IEEE Trans. Image Process. 26(4), 1579–1594 (2017).  https://doi.org/10.1109/TIP.2017.2663846MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Serra, J.: Image Analysis and Mathematical Morphology: Theoretical Advances, vol. 2. Academic Press, London (1988)Google Scholar
  17. 17.
    Serra, J., Cressie, N.: Image Analysis and Mathematical Morphology, vol. 1. Academic Press, London (1982)Google Scholar
  18. 18.
    Sugimura, D., et al.: Enhancing color images of extremely low light scenes based on RGB/NIR images acquisition with different exposure times. IEEE Trans. Image Process. 24(11), 3586–3597 (2015).  https://doi.org/10.1109/TIP.2015.2448356MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of Strathclyde Institute of Global Public HealthEcullyFrance
  2. 2.International Prevention Research Institute, iPRILyonFrance

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