Skip to main content
SpringerLink
Log in

Error-Tolerant Color Rendering for Digital Cameras

  • Published:
Journal of Mathematical Imaging and Vision Aims and scope Submit manuscript

Abstract

In digital cameras a color processing pipeline is implemented to convert the RAW image acquired by the camera sensor into a faithful representation of the original scene. There are two main modules in this pipeline: the former is the illuminant estimation and correction module, the latter is the color matrix transformation. In this work we design extended color correction pipelines which exploit the crosstalks between their modules to lead to a higher color rendition accuracy. The effectiveness of the proposed pipelines is shown on a publicly available dataset of RAW images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Algorithm 1
Algorithm 2
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Sharma, G.: Digital Color Imaging Handbook. CRC Press, Boca Raton (2002)

    Book  Google Scholar 

  2. Ramanath, R., Snyder, W.E., Yoo, Y., Drew, M.S.: Color image processing pipeline. IEEE Signal Process. Mag. 22(1), 34–43 (2005)

    Article  Google Scholar 

  3. Adams, J.E., Hamilton, J.F.: Digital Camera Image Processing Chain Design. Single-Sensor Imaging. CRC Press, Boca Raton (2009)

    Google Scholar 

  4. Hordley, S.D.: Scene illuminant estimation: past, present, and future. Color Res. Appl. 31(4), 303–314 (2006)

    Article  Google Scholar 

  5. Ebner, M.: Color Constancy. Wiley, London (2007)

    Google Scholar 

  6. Foster, D.H.: Color constancy. Vis. Res. 51, 674–700 (2011)

    Article  Google Scholar 

  7. Gijsenij, A., Gevers, T., van de Weijer, J.: Computational color constancy: survey and experiments. IEEE Trans. Image Process. 20(9), 2475–2489 (2011)

    Article  MathSciNet  Google Scholar 

  8. Finlayson, G.D., Drew, M.S.: Constrained least-squares regression in color spaces. J. Electron. Imaging 6, 484–493 (1997)

    Article  Google Scholar 

  9. Hubel, P.M., Holm, J., Finlayson, G.D., Drew, M.S.: Matrix calculations for digital photography. In: Proceedings of the IS&T/SID 5th Color Imaging Conference, pp. 105–111 (1997)

    Google Scholar 

  10. Bianco, S., Gasparini, F., Russo, A., Schettini, R.: A new method for RGB to XYZ transformation based on pattern search optimization. IEEE Trans. Consum. Electron. 53(3), 1020–1028 (2007)

    Article  Google Scholar 

  11. Burns, P.D., Berns, R.S.: Error propagation analysis in color measurement and imaging. Color Res. Appl. 22(4), 280–289 (1997)

    Article  Google Scholar 

  12. Bianco, S., Bruna, A., Naccari, F., Schettini, R.: Color space transformations for digital photography exploiting information about the illuminant estimation process. J. Opt. Soc. Am. A 29(3), 374–384 (2012)

    Article  Google Scholar 

  13. von Kries, J.: Chromatic Adaptation. Festschrift der Albrecht-Ludwig-Universität, Fribourg (1902). Translation: MacAdam, D.L.: Sources of color science. MIT Press, Cambridge (1970)

    Google Scholar 

  14. Finlayson, G.D., Hordley, S.D.: Color by correlation: a simple, unifying framework for color constancy. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1209–1221 (2001)

    Article  Google Scholar 

  15. Bianco, S., Gasparini, F., Schettini, R.: Consensus based framework for illuminant chromaticity estimation. J. Electron. Imaging 17(2), 023013 (2008)

    Article  Google Scholar 

  16. Finlayson, G.D.: Color constancy in diagonal chromaticity space. In: IEEE Proc. 5th International Conference on Computer Vision, pp. 218–223 (1995)

    Chapter  Google Scholar 

  17. Van De Weijer, J., Gevers, T., Gijsenij, A.: Edge-based color constancy. IEEE Trans. Image Process. 16(9), 2207–2214 (2007)

    Article  MathSciNet  Google Scholar 

  18. Buchsbaum, G.: A spacial processor model for object color perception. J. Franklin Inst. 310, 1–26 (1980)

    Article  Google Scholar 

  19. Cardei, V., Funt, B., Barnard, K.: White point estimation for uncalibrated images. In: Proceedings of the IS&T/SID 7th Color Imaging Conference, pp. 97–100 (1999)

    Google Scholar 

  20. Finlayson, G., Trezzi, E.: Shades of gray and colour constancy. In: Proceedings IS&T/SID 12th Color Imaging Conference, pp. 37–41 (2004)

    Google Scholar 

  21. Bianco, S., Ciocca, G., Cusano, C., Schettini, R.: Improving color constancy using indoor-outdoor image classification. IEEE Trans. Image Process. 17(12), 2381–2392 (2008)

    Article  MathSciNet  Google Scholar 

  22. Bianco, S., Ciocca, G., Cusano, C., Schettini, R.: Automatic color constancy algorithm selection and combination. Pattern Recognit. 43, 695–705 (2010)

    Article  MATH  Google Scholar 

  23. Gehler, P.V., Rother, C., Blake, A., Minka, T., Sharp, T.: Bayesian color constancy revisited. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), pp. 1–8 (2008)

    Google Scholar 

  24. Pascale, D.: RGB coordinates of the Macbeth ColorChecker, pp. 1–16 (2006). http://www.babelcolor.com/download/

  25. Hartigan, J.A., Wong, M.A.: Algorithm AS136: a k-means clustering algorithm. Appl. Stat. 28, 100–108 (1979)

    Article  MATH  Google Scholar 

  26. Bianco, S., Schettini, R., Vanneschi, L.: Empirical modeling for colorimetric characterization of digital cameras. In: Proceedings of IEEE International Conference on Image Processing (ICIP’09), pp. 3469–3472 (2009)

    Google Scholar 

  27. Forsyth, D.A.: A novel algorithm for color constancy. Int. J. Comput. Vis. 5(1), 5–36 (1990)

    Article  MathSciNet  Google Scholar 

  28. Shi, L., Funt, B.V.: Re-processed Version of the Gehler Color Constancy Database of 568 Images. Available online http://www.cs.sfu.ca/~colour/data/. Last access: 22 May, 2012

  29. Finlayson, G.D., Mackiewicz, M., Hurlbert, A.: Root-polynomial colour correction. In: Proceedings of the IS&T/SID 19th Color and Imaging Conference, pp. 115–119 (2011)

    Google Scholar 

  30. Bianco, S., Gasparini, F., Schettini, R., Vanneschi, L.: Polynomial modeling and optimization for colorimetric characterization of scanners. J. Electron. Imaging 17(4), 043002 (2008)

    Article  Google Scholar 

  31. Bianco, S., Cusano, C.: Color target localization under varying illumination conditions. In: Computational Color Imaging Workshop 2011. LNCS, vol. 6626, pp. 245–255 (2011)

    Google Scholar 

  32. Kang, H.R.: Computational Color Technology, vol. PM159. SPIE Press, Bellingham (2006)

    Book  Google Scholar 

  33. Finlayson, G.D., Drew, M.S., Funt, B.V.: Color constancy: generalized diagonal transforms suffice. J. Opt. Soc. Am. A 11(11), 3011–3019 (1994)

    Article  Google Scholar 

  34. Bianco, S., Schettini, R.: Computational color constancy. In: 3rd European Workshop of Visual Information Processing (EUVIP), pp. 1–7 (2011)

    Chapter  Google Scholar 

  35. Fairchild, M.D.: Color Appearance Models. Addison-Wesley, Reading (1998)

    Google Scholar 

  36. Bianco, S., Schettini, R.: Two new von Kries based chromatic adaptation transforms found by numerical optimization. Color Res. Appl. 35(3), 184–192 (2010)

    Article  Google Scholar 

  37. Smoyer, E.P., Taplin, L.A., Berns, R.S.: Experimental evaluation of museum case study digital camera systems. In: Proceedings of the IS&T 2005 Archiving Conference, pp. 85–89 (2005)

    Google Scholar 

  38. Lewis, R.M., Torczon, V.: Pattern search algorithms for bound constrained minimization. SIAM J. Control Optim. 9, 1082–1099 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  39. Lewis, R.M., Torczon, V.: Pattern search methods for linearly constrained minimization. SIAM J. Control Optim. 10, 917–941 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  40. Funt, B., Jiang, H.: Nondiagonal color correction. In: Proceedings of the 2003 International Conference on Image Processing (ICIP 2003), pp. 481–484 (2003)

    Google Scholar 

  41. Land, E.H.: The retinex theory of color vision. Sci. Am. 237(6), 108–128 (1977)

    Article  Google Scholar 

  42. Ebner, M.: Color constancy based on local space average color. Mach. Vis. Appl. 20(5), 283–301 (2009)

    Article  Google Scholar 

  43. Bleier, M., Riess, C., Beigpour, S., Eibenberger, E., Angelopoulou, E., Troger, T., Kaup, A.: Color constancy and non-uniform illumination: can existing algorithms work? In: IEEE International Conference on Computer Vision Workshops, pp. 774–781 (2011)

    Google Scholar 

  44. Gijsenij, A., Lu, R., Gevers, T.: Color constancy for multiple light sources. IEEE Trans. Image Process. 21(2), 697–707 (2012)

    Article  MathSciNet  Google Scholar 

  45. Bianco, S., Schettini, R.: Adaptive color constancy using faces. IEEE Trans. Pattern Anal. Mach. Intell. (2014). doi:10.1109/TPAMI.2013.2297710

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126, Milano, Italy

    Simone Bianco & Raimondo Schettini

Authors
  1. Simone Bianco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raimondo Schettini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Simone Bianco.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bianco, S., Schettini, R. Error-Tolerant Color Rendering for Digital Cameras. J Math Imaging Vis 50, 235–245 (2014). https://doi.org/10.1007/s10851-014-0496-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10851-014-0496-1

Keywords

Search

Navigation