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T-Rex: A Milano Retinex Implementation Based on Intensity Thresholding

  • Michela LeccaEmail author
  • Carla M. Modena
  • Alessandro Rizzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10213)

Abstract

We present T-Rex (from the words Threshold and REtineX), a new Milano Retinex implementation, based on an intensity thresholding strategy. Like all the algorithms of the Retinex family, T-Rex takes as input a color image and processes its channels separately. For each channel, T-Rex re-scales the chromatic intensity of each pixel x by the average of a set of pixels whose intensity, weighted by a function of the distance from x, exceeds the intensity of x. The main novelty of this approach is devised by the usage of the pixel intensity as a threshold for selecting the pixels relevant to Retinex. Here we show an application of T-Rex as image enhancer, showing that, as a member of the Retinex family, it equalizes the dynamic range of any input picture and makes its details more evident.

Keywords

Input Image Image Enhancement Sampling Figure Color Sensation Image Enhancer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Banic, N., Loncaric, S.: Light random sprays retinex: exploiting the noisy illumination estimation. IEEE Signal Process. Lett. 20(12), 1240–1243 (2013)CrossRefGoogle Scholar
  2. 2.
    Creutzfeldt, O., Lange-Malecki, B., Wortmann, K.: Darkness induction, retinex and cooperative mechanisms in vision. Exp. Brain Res. 67(2), 270–283 (1987)CrossRefGoogle Scholar
  3. 3.
    Gianini, G., Lecca, M., Rizzi, A.: A population based approach to point-sampling spatial color algorithms. J. Opt. Soc. Am. A 33(12), 2396–2413 (2016)CrossRefGoogle Scholar
  4. 4.
    Gianini, G., Manenti, A., Rizzi, A.: QBRIX: a quantile-based approach to retinex. J. Opt. Soc. Am. A 31(12), 2663–2673 (2014)CrossRefGoogle Scholar
  5. 5.
    Gianini, G., Rizzi, A., Damiani, E.: A retinex model based on absorbing markov chains. Inf. Sci. 327, 149–174 (2016)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Kolås, Ø., Farup, I., Rizzi, A.: Spatio-temporal retinex-inspired envelope with stochastic sampling: a framework for spatial color algorithms. J. Imaging Sci. Technol. 55(4), 40503-1–40503-10 (2011)Google Scholar
  7. 7.
    Land, E.H., McCann, J.J.: Lightness and retinex theory. J. Opt. Soc. Am. 1, 1–11 (1971)CrossRefGoogle Scholar
  8. 8.
    Lecca, M., Rizzi, A.: Tuning the locality of filtering with a spatially weighted implementation of random spray retinex. JOSA A 32(10), 1876–1887 (2015)CrossRefGoogle Scholar
  9. 9.
    Lecca, M., Rizzi, A., Gianini, G.: Energy-driven path search for termite retinex. JOSA A 33(1), 31–39 (2016)CrossRefGoogle Scholar
  10. 10.
    Marini, D., Rizzi, A.: Color constancy and optical illusions: a computer simulation with Retinex theory. In: ICIAP 1993 7th International Conference on Image Analysis and Processing, Monopoli, Italy, pp. 657–660 (1993)Google Scholar
  11. 11.
    Marini, D., Rizzi, A.: A computational approach to color adaptation effects. Image Vis. Comput. 18(13), 1005–1014 (2000)CrossRefGoogle Scholar
  12. 12.
    McCann, J., Rizzi, A.: The Art and Science of HDR Imaging. Wiley, New York (2011)CrossRefGoogle Scholar
  13. 13.
    McCann, J.J., (ed.): Special session on retinex at 40. J. Electron. Imaging 13(1), 6–145 (2004)Google Scholar
  14. 14.
    Montagna, R., Finlayson, G.D.: Constrained Pseudo-Brownian motion and its application to image enhancement. J. Opt. Soc. Am. A 28(8), 1677–1688 (2011)CrossRefGoogle Scholar
  15. 15.
    Provenzi, E., De Carli, E., Rizzi, A., Marini, D.: Mathematical definition and analysis of the retinex algorithm. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 22(12), 2613–2621 (2005)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Provenzi, E., Fierro, M., Rizzi, A., De Carli, L., Gadia, D., Marini, D.: Random spray retinex: a new retinex implementation to investigate the local properties of the model. Trans. Img. Proc. 16(1), 162–171 (2007)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Rizzi, A.: Designator retinex, milano retinex and the locality issue. Electron. Imaging 2016(6), 1–5 (2016)CrossRefGoogle Scholar
  18. 18.
    Rizzi, A., Algeri, T., Medeghini, G., Marini, D.: A proposal for contrast measure in digital images. In: CGIV 2004 - Second European Conference on Color in Graphics, Imaging, and Vision and Sixth International Symposium on Multispectral Color Science, Aachen, pp. 187–192 (2004)Google Scholar
  19. 19.
    Rizzi, A., McCann, J.J.: Computer algorithms that mimic human vision must respond to the spatial content in images. In: SPIE Electronic Imaging & Signal Processing (2007)Google Scholar
  20. 20.
    Rizzi, A., McCann, J.J.: On the behavior of spatial models of color. In: Proceedings of SPIE - The International Society for Optical Engineering, San Jose, CA, vol. 6493 (2007)Google Scholar
  21. 21.
    Rizzi, A., McCann, J.J., Bertalmio, M., Gianini, G. (eds.): Retinex at 50. Special issue on Journal of Electronic Imaging, vol. 26(3) (2017)Google Scholar
  22. 22.
    Simone, G., Audino, G., Farup, I., Albregtsen, F., Rizzi, A.: Termite retinex: a new implementation based on a colony of intelligent agents. J. Electron. Imaging 23(1), 013006 (2014)Google Scholar
  23. 23.
    Simone, G., Cordone, R., Lecca, M., Serapioni, R.P.: On edge-aware path-based color spatial sampling for retinex: from termite retinex to light-energy driven termite retinex. J. Electron. Imaging 26(3), 031203 (2017). Special Issue, Retinex at 50Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Michela Lecca
    • 1
    Email author
  • Carla M. Modena
    • 1
  • Alessandro Rizzi
    • 2
  1. 1.Center for Information and Communication Technology, Technologies of VisionFondazione Bruno KesslerTrentoItaly
  2. 2.Dipartimento di InformaticaUniversitá degli Studi di MilanoMilanoItaly

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