Skip to main content
SpringerLink
Log in

Saturation avoidance color correction for digital color images

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The qualities of color images captured by digital imaging devices are vulnerable to the scene illumination settings of a given environment. The colors of captured objects may not be accurately reproduced when the illumination settings are uncontrollable or not known a priori. This undesirable property can inevitably degrade the qualities of captured images and lead to difficulties in subsequent image-processing stages. Considering that the task of controlling scene illumination is nontrivial, color correction has emerged as a plausible post-processing procedure to efficiently restore the scene chromatics of a given image. In this study, a new color correction technique called the Saturation Avoidance Color Correction (SACC) algorithm is proposed to remove the undesirable effect of scene illuminants. Unlike most well-established color correction algorithms, the proposed SACC comprises a nonlinear pixel adjustment mechanism to avoid the saturation effect during the color manipulation process. A collection of color images including indoor, outdoor, and underwater images are used to verify the capability of SACC. Extensive experimental studies reveal that the proposed algorithm is preferable to some existing techniques because the former has a high capability to mitigate the color saturation issue and is able to produce corrected images with more pleasant visualization.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Abdul Ghani AS, Mat Isa NA (2015) Enhancement of low quality underwater image through integrated global and local contrast correction. Appl Soft Comput 37:332–344

    Article  Google Scholar 

  2. Agarwal V, Gribok AV, Abidi MA (2007) Machine learning approach to color constancy. Neural Netw 20:559–563

    Article  MATH  Google Scholar 

  3. Ayache N (1995) Medical computer vision, virtual reality and robotics. Image Vis Comput 13:295–313

    Article  Google Scholar 

  4. Barnard K, Cardei V, Funt B (2002) A comparison of computational color constancy algorithms. I: methodology and experiments with synthesized data. IEEE Trans Image Process 11:972–984

    Article  Google Scholar 

  5. Barnard K, Martin L, Coath A, Funt B (2002) A comparison of computational color constancy algorithms. II. Experiments with image data. IEEE Trans Image Process 11:985–996

    Article  Google Scholar 

  6. Barnard K, Martin L, Funt B (2000) Colour by correlation in a three-dimensional colour space. In Computer Vision - ECCV 2000. vol. 1842, ed: Springer Berlin Heidelberg, pp. 375–389

  7. Bianco S, Ciocca G, Cusano C, Schettini R (2008) Classification-based color constancy. In: Sebillo M, Vitiello G, Schaefer G (eds) Visual information systems. Web-based visual information search and management. Springer, Berlin, pp 104–113

    Chapter  Google Scholar 

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

    Article  MATH  Google Scholar 

  9. Bianconi F, Ceccarelli L, Fernández A, Saetta SA (2014) A sequential machine vision procedure for assessing paper impurities. Comput Ind 65:325–332

    Article  Google Scholar 

  10. Buchsbaum G (1980) A spatial processor model for object colour perception. J Franklin Inst 310:1–26

    Article  Google Scholar 

  11. Cardei V (2000) A neural network approach to color constancy. PhD Thesis, Simon Fraser Univ

  12. Chen SY, Li YF, Zhang J (2008) Vision processing for realtime 3-D data acquisition based on coded structured light. IEEE Trans Image Process 17:167–176

    Article  MathSciNet  Google Scholar 

  13. Chen C-L, Lin S-H (2011) Formulating and solving a class of optimization problems for high-performance gray world automatic white balance. Appl Soft Comput 11:523–533

    Article  Google Scholar 

  14. Cheng Y, Jafari MA (2008) Vision-based online process control in manufacturing applications. IEEE Trans Autom Sci Eng 5:140–153

    Article  Google Scholar 

  15. Doulamis A, Doulamis N, Kollas S (2000) Non-sequential video content representation using temporal variation of feature vectors. Consum Electron IEEE Trans 46:758–768

    Article  Google Scholar 

  16. Eberhardt M, Roth S, König A (2008) Industrial application of machine-in-the-loop-learning for a medical robot vision system – Concept and comprehensive field study. Comput Electr Eng 34:111–126

    Article  Google Scholar 

  17. Faghih MM, Moghaddam ME (2014) Multi-objective optimization based color constancy. Appl Soft Comput 17:52–66

    Article  Google Scholar 

  18. Finlayson G, Hordley S (2000) Improving gamut mapping color constancy. IEEE Trans Image Process 9:1774–1783

    Article  Google Scholar 

  19. Finlayson GD, Hordley SD, Hubel PM (2001) Color by correlation: a simple, unifying framework for color constancy. IEEE Trans Pattern Anal Mach Intell 23:1209–1221

    Article  Google Scholar 

  20. Finlayson GD, Hordley SD, Tastl I (2006) Gamut constrained illuminant estimation. Int J Comput Vis 67:93–109

    Article  Google Scholar 

  21. Finlayson GD and Hubel PH (2000) White point estimation using color by correlation. U.S. Patent US6038339 A Patent

  22. Finlayson GD, Trezzi E (2004) Shades of gray and colour constancy, presented at the Twelfth Color Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications, Scottsdale, Arizona, USA

  23. Gasparini F, Schettini R (2004) Color balancing of digital photos using simple image statistics. Pattern Recogn 37:1201–1217

    Article  Google Scholar 

  24. Gijsenij A, Gevers T (2007) Color constancy by local averaging. In 14th International Conference on Image Analysis and Processing Workshops (ICIAPW 2007), pp. 171–174

  25. Gijsenij A, Gevers T (2011) Color constancy using natural image statistics and scene semantics. IEEE Trans Pattern Anal Mach Intell 33:687–698

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  27. Huo J-y, Chang Y-l, Wang J, Wei X-x (2006) Robust automatic white balance algorithm using gray color points in images. IEEE Trans Consum Electron 52:541–546

    Article  Google Scholar 

  28. Kim B-K, Park R-H (2010) Detection and correction of purple fringing using color desaturation in the xy chromaticity diagram and the gradient information. Image Vis Comput 28:952–964

    Article  Google Scholar 

  29. Kurtulmuş F, Kavdir İ (2014) Detecting corn tassels using computer vision and support vector machines. Expert Syst Appl 41:7390–7397

    Article  Google Scholar 

  30. Kwok NM, Shi HY, Ha QP, Fang G, Chen SY, Jia X (2013) Simultaneous image color correction and enhancement using particle swarm optimization. Eng Appl Artif Intel 26:2356–2371

    Article  Google Scholar 

  31. Kwok NM, Wang D, Jia X, Chen SY, Fang G, Ha QP (2011) Gray world based color correction and intensity preservation for image enhancement. In 4th International Congress on Image and Signal Processing (CISP 2011), pp. 994–998

  32. Lam EY (2005) Combining gray world and retinex theory for automatic white balance in digital photography. In Ninth International Symposium on the Proceedings of Consumer Electronics (ISCE 2005), pp. 134–139

  33. Land E (1977) The retinex theory of color vision. Sci Am 237:108–128

    Article  Google Scholar 

  34. Land EH, McCann JJ (1971) Lightness and retinex theory. J Opt Soc Am 61:1–11

    Article  Google Scholar 

  35. Lee J-H, Yoo H-S, Kim Y-S, Lee J-B, Cho M-Y (2006) The development of a machine vision-assisted, teleoperated pavement crack sealer. Autom Constr 15:616–626

    Article  Google Scholar 

  36. Montenegro J, Gomez W, Sanchez-Orellana P (2013) A comparative study of color spaces in skin-based face segmentation. In 10th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE 2013), pp. 313–317

  37. Nammi S, Ramamoorthy B (2014) Effect of surface lay in the surface roughness evaluation using machine vision. Optik Int J Light Electron Opt 125:3954–3960

    Article  Google Scholar 

  38. Nashat S, Abdullah A, Abdullah MZ (2014) Machine vision for crack inspection of biscuits featuring pyramid detection scheme. J Food Eng 120:233–247

    Article  Google Scholar 

  39. Nikitenko D, Wirth M, Trudel K (2008) Applicability of white-balancing algorithms to restoring faded colour slides: an empirical evaluation. J Multimed 3:9–18

    Article  Google Scholar 

  40. Quintana J, Garcia R, Neumann L (2011) A novel method for color correction in epiluminescence microscopy. Comput Med Imaging Graph 35:646–652

    Article  Google Scholar 

  41. Rajamani V, Babu P, Rajinikannan M (2013) Optimal histogram modification scheme for image contrast enhancement using Otsu’s optimality method. In 2013 International Conference Green Computing, Communication and Conservation of Energy (ICGCE), Chennai, pp. 100–105

  42. Raju A, Dwarakish GS, Reddy DV (2013) Adaptive plateau histogram equalization with mean threshold for brightness preserving and contrast enhancement. In 2013 I.E. Second International Conference on Image Information Processing (ICIIP), Shimla, pp. 208–213

  43. Recky M, Leberl F (2010) Windows detection using K-means in CIE-Lab color space. In 20th International Conference on Pattern Recognition (ICPR 2010), pp. 356–359

  44. Schechner YY, Karpel N (2005) Recovery of underwater visibility and structure by polarization analysis. IEEE J Ocean Eng 30:570–587

    Article  Google Scholar 

  45. Stanikunas R, Vaitkevicius H, Kulikowski JJ (2004) Investigation of color constancy with a neural network. Neural Netw 17:327–337

    Article  MATH  Google Scholar 

  46. van de Weijer J, Gevers T (2005) Color constancy based on the Grey-edge hypothesis. In IEEE International Conference on Image Processing (ICIP 2005), pp. II-722–5

  47. Weng C-C, Chen H, Fuh C-S (2005) A novel automatic white balance method for digital still cameras. IEEE Int Symp Circuits Syst 4:3801–3804

    Article  Google Scholar 

  48. Wirth Mm Nikitenko D (2010) The effect of colour space on image sharpening algorithms. In Canadian Conference on Computer and Robot Vision (CRV 2010), pp. 79–85

  49. Yuan J-Z, Tian L-Y, Bao H, Huang J-H, Zhang R-Z (2009) llumination estimation combining physical and statistical approaches. In Third International Symposium on Intelligent Information Technology Application (IITA 2009), pp. 365–368

  50. Zhang J, Yang Y, Zhang J (2016) A MEC-BP-Adaboost neural network-based color correction algorithm for color image acquisition equipments. Optik Int J Light Electron Opt 127:776–780

    Article  Google Scholar 

  51. Zhuo L, Zhang J, Dong P, Zhao Y, Peng B (2014) An SA–GA–BP neural network-based color correction algorithm for TCM tongue images. Neurocomputing 134:111–116

    Article  Google Scholar 

  52. Zhuo L, Zhang P, Qu P, Peng Y, Zhang J, Li X (2016) A K-PLSR-based color correction method for TCM tongue images under different illumination conditions. Neurocomputing 174(Part B):815–821

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to express their sincere gratitude to the associate editor and all reviewers who made great contributions to the improvement of this paper. This research was supported by the Fundamental Research Grant Scheme (“Formulation of a Robust Framework of Image Enhancement for Nonuniform Illumination and Low-Contrast Images”) of the Ministry of Higher Education of Malaysia.

Author information

Authors and Affiliations

  1. Imaging and Intelligent System Research Team (ISRT), School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300, Nibong Tebal, Penang, Malaysia

    Mohd Naim Mohd Jain Noordin, Nor Ashidi Mat Isa & Wei Hong Lim

Authors
  1. Mohd Naim Mohd Jain Noordin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nor Ashidi Mat Isa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Hong Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nor Ashidi Mat Isa.

Appendices

Appendix A. Color correction results of the additional 18 test images from the GW, WP, gGW, MGE, GbC, DIR-CLAS, and SACC techniques

Fig. 17
figure 17

Original and resultant indoor images of In1 to In6 after applying GW, WP, gGW, MGE, GbC, and the proposed SACC

Full size image
Fig. 18
figure 18

Original and resultant outdoor images of Ot1 to Ot6 after applying GW, WP, gGW, MGE, GbC, and the proposed SACC

Full size image
Fig. 19
figure 19

Original and resultant underwater images of UW1 to UW6 after applying GW, WP, gGW, MGE, GbC, and the proposed SACC

Full size image

Appendix B. Comparison of the color correction results from the GW, WP, gGW, MGE, GbC, and SACC techniques based on the AMBE evaluation metric

Table 5 AMBE values of the 18 additional corrected indoor, outdoor, and underwater images
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohd Jain Noordin, M.N., Mat Isa, N.A. & Lim, W.H. Saturation avoidance color correction for digital color images. Multimed Tools Appl 76, 10279–10312 (2017). https://doi.org/10.1007/s11042-016-3620-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3620-y

Keywords

Search

Navigation