Journal of Real-Time Image Processing

, Volume 1, Issue 4, pp 267–283 | Cite as

Algorithmic and architectural design for real-time and power-efficient Retinex image/video processing

  • Sergio Saponara
  • Luca Fanucci
  • Stefano Marsi
  • Giovanni Ramponi
Original Research Paper


This paper presents novel algorithmic and architectural solutions for real-time and power-efficient enhancement of images and video sequences. A programmable class of Retinex-like filters, based on the separation of the illumination and reflectance components, is proposed. The dynamic range of the input image is controlled by applying a suitable non-linear function to the illumination, while the details are enhanced by processing the reflectance. An innovative spatially recursive rational filter is used to estimate the illumination. Moreover, to improve the visual quality results of two-branch Retinex operators when applied to videos, a novel three-branch technique is proposed which exploits both spatial and temporal filtering. Real-time implementation is obtained by designing an Application Specific Instruction-set Processor (ASIP). Optimizations are addressed at algorithmic and architectural levels. The former involves arithmetic accuracy definition and linearization of non-linear operators; the latter includes customized instruction set, dedicated memory structure, adapted pipeline, bypasses, custom address generator, and special looping structures. The ASIP is synthesized in standard-cells CMOS technology and its performances are compared to known Digital signal processor (DSP) implementations of real-time Retinex filters. As a result of the comparison, the proposed algorithmic/architectural design outperforms state-of-art Retinex-like operators achieving the best trade-off between power consumption, flexibility, and visual quality.


Application specific instruction-set processors (ASIP) Digital signal processor (DSP) Retinex Image enhancement Real-time image and video filters 



The contribution to the LISA design of M. Cassiano from University of Pisa, now with STMicroelectronics, Italy, and D. Kammler, E. M. Witte, O. Schliebusch, G. Ascheid, R. Leupers and H. Meyr from ISS, RWTH Aachen University, Germany, is gratefully acknowledged. The work performed at the University of Trieste was partially supported by a research grant of the Regione Autonoma Friuli—Venezia Giulia. The work performed at the University of Pisa was partially supported by the Network of Excellence in Wireless COMmunications (NEWCOM).


  1. 1.
    Land, E., McCann, J.: Lightness and retinex theory. J. Opt. Soc. Am. 61(1), 1–11 (1971)CrossRefGoogle Scholar
  2. 2.
    Land, E.: An alternative technique for the computation of the designator in the retinex theory of color vision. Proc. Natl. Acad. Sci. 83, 3078–3080 (1986)CrossRefGoogle Scholar
  3. 3.
    Jobson, D.J., Rahman, Z., Woodell, G.A.: Properties and performance of a center/surround retinex. IEEE Trans. Image Process. 6(3), 451–462 (1997)CrossRefGoogle Scholar
  4. 4.
    Ogata, M., Tsuchiya, T., Kubozono, T., Ueda, K.: Dynamic range compression based on illumination compensation. IEEE Trans. Consum. Electron. 47(3), 548–558 (2001)CrossRefGoogle Scholar
  5. 5.
    Rahman, Z., Jobson, D.J., Woodell, G.A., Hines, G.D.: Impact of multiscale retinex computation on performance of segmentation algorithms. In: Proceedings of the SPIE 5438: Visual Information Processing XIII, Orlando, USA, pp. 171–182 (2004)Google Scholar
  6. 6.
    Jobson, D.J., Rahman, Z., Woodell, G.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process. 6(7), 965–976 (1997)CrossRefGoogle Scholar
  7. 7.
    Tao, L., Tompkins, R., Asari, V.K.: An illuminance-reflectance nonlinear video enhancement model for homeland security applications. In: Proceedings of the 34th Applied Imagery and Pattern Recognition Workshop, Washington DC, USA, pp. 28–35, 19–21 October (2005)Google Scholar
  8. 8.
    Tao, L., Tompkins R., Asari, V.: An illuminance-reflectance model for nonlinear enhancement of color images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, USA, pp. 159–167 (2005)Google Scholar
  9. 9.
    Nam, M., Rhee, P.: An efficient face recognition for variant illumination condition. In: Proceedings of the IEEE International Symposium Intelligent Signal Processing and Communication Systems (ISPACS), Seoul, Korea, pp. 111–115, 18–19 November (2004)Google Scholar
  10. 10.
    Wang, H., Li, S.Z., Wang, Y., Zhang, J.: Self quotient image for face recognition. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), Singapore, vol. 2, pp. 1397–1400, 24–27 October (2004)Google Scholar
  11. 11.
    Huang, J., Xie, W., Tang, L.: Detection of and compensation for shadows in colored urban aerial images. In: Proceedings of the 5th World Congress On Intelligent Control and Automation (WCICA), Hangzhou, China, vol. 4, pp. 3098–3100, 15–19 June (2004)Google Scholar
  12. 12.
    Lee, I., Ko, H., Han, D.K.: Multiple vehicle tracking based on regional estimation in night time CCD images. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002 (ICASSP ’02), Orlando, USA, vol. 4, pp. 3712–3715, 13–17 May (2002)Google Scholar
  13. 13.
    Iqbal, N.: Automatic enhancement of chest radiography using-retinex processing. In: Proceedings of the International Multi Topic Conference (INMIC), Karachi, Pakistan, pp. 27–31, 27–28 December (2002)Google Scholar
  14. 14.
    Hines, G.D., Rahman, Z., Jobson, D.J., Woodell, G.A.: DSP implementation of the retinex image enhancement algorithm. In: Proceedings of the SPIE 5438: Visual Information Processing XIII, Orlando, USA, pp. 13–24 (2004)Google Scholar
  15. 15.
    Hines, G., Rahman, Z., Jobson, D.J., Woodell, G.A., Harrah, S.D.: Real-time enhanced vision system. In: Proceedings of the SPIE 5802: Enhanced and Synthetic Vision 2005, Orlando, USA, pp. 127–134 (2005)Google Scholar
  16. 16.
    Marsi, S., Carrato, S., Ramponi, G.: Image contrast enhancement using a recursive rational filter. In: Proceedings of the IEEE IST 2004, Stresa, pp. 29–34 (2004)Google Scholar
  17. 17.
    Orsini, G., Ramponi, G., Carrai, P., Di Federico, R.: A modified retinex for image contrast enhancement and dynamics control. In: Proceedings of the IEEE International Conference on Image Processing, ICIP 2003, Barcelona, Spain, pp. 393–396, 14–17 September (2003)Google Scholar
  18. 18.
    Funt, B., Ciurea, F., McCann, J.: Retinex in matlab. J. Electron. Imaging 13(1), 48–57 (2004)CrossRefGoogle Scholar
  19. 19.
    Ciurea, F., Funt, B.: Tuning retinex parameters. J. Electron. Imaging 13(1), 58–64 (2004)Google Scholar
  20. 20.
    Rahman, Z., Jobson, D.J., Woodell, G.A.: Retinex processing for automatic image enhancement. J. Electron. Imaging 13(1) (2004)Google Scholar
  21. 21.
    NASA Langley Research Center: (website accessed on 20 April 2007)
  22. 22.
    Ramponi, G.: Polynomial and rational operators for image processing and analysis. In: Mitra, S., Sicuranza, G. (eds.) Nonlinear Image Processing. Academic, New York (2000)Google Scholar
  23. 23.
    Rabiner, L.R., Gold, B.: Theory and Application of Digital Signal Processing. Prentice-Hall International, Englewood Cliffs, NJ (1975)Google Scholar
  24. 24.
    Fattal, R., Lischinski, D., Werman, M.: Gradient domain high dynamic range compression. ACM Trans. Graph. 21(3), 249–256 (2002)CrossRefGoogle Scholar
  25. 25.
    Saponara, S., Cassiano, M., Marsi, S., Coen, R., Fanucci, L.: Cost-effective VLSI design of non linear image processing filters. In: Proceedings of the IEEE 8th Euromicro Conference—Digital System Design, Porto, Portugal, pp. 322–329 (2005)Google Scholar
  26. 26.
    Schliebusch, O., Chattopadhyay, A., Witte, E.M., Kammler, D., Ascheid, G., Leupers, R., Meyr, H., et al.: Optimization techniques for ADL-driven RTL processor synthesys. In: Proceedings of the IEEE Workshop on Rapid Prototyping Systems, Montreal, pp. 165–171 (2005)Google Scholar
  27. 27.
    Peters, H., Sethuraman, R, Beric, A., Meuwissen, P., Balakrishnan, S., Pinto, C., Kruijtzer, W., Ernst, F., Alkadi, G., van Meerbergen, J., de Haan, G.: Application specific instruction-set processor template for motion estimation in video applications. IEEE Trans. Circuits Syst. Video Tech. 15, 508–527 (2005)CrossRefGoogle Scholar
  28. 28.
    Hoffmann, A., Leupers, R., Meyr, H.: Architecture Exploration for Embedded Processors with LISA. Kluwer, Dordrecht (2002)MATHGoogle Scholar
  29. 29.
    Schliebusch, O., Chattopadhyay, A., Kammler, D., Ascheid, G., Leupers, R., Meyr, H., Kogel, T.: A framework for automated and optimized ASIP implementation supporting multiple hardware description languages. In: Proceedings of the IEEE ASP-DAC 2005, Shanghai, pp. 280–285 (2005)Google Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Sergio Saponara
    • 1
  • Luca Fanucci
    • 1
  • Stefano Marsi
    • 2
  • Giovanni Ramponi
    • 2
  1. 1.Dipartimento Ingegneria della InformazioneUniversity of PisaPisaItaly
  2. 2.Dipartimento Elettrotecnica, Elettronica, InformaticaUniversity of TriesteTriesteItaly

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