Skip to main content
Springer Nature Link
Log in

Nonlinear local-pixel-shifting color constancy algorithm

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

Abstract

Most color constancy algorithms implement a color correction process that performs globally for the entire input pixel image. This process leads to a saturation problem and overcorrected images, especially in overexposed regions, thereby resulting in contrast inconsistency and incorrect true-color image objects. Each pixel in an input image should have a different correction range value. Pixels with high probability to be saturated or overcorrected should not be treated similarly as pixels with low probability. Thus, this study proposes a new color correction algorithm that aims to reduce the effects of the saturation phenomenon and overcorrected color images while enhancing the image contrast. To achieve this objective, the proposed algorithm consists of two main processes, namely, color correction and contrast enhancement. A shifting process is nonlinearly applied to each pixel in a 2D two-channel CIELAB color space. Each pixel has a different corrected rate depending on the adaptive limit value and the reference point. Subsequently, the global and local contrast corrections are executed on the L channel to enhance the image contrast. Qualitative and quantitative results on 653 outdoor; 818 indoor; and 1394 underwater images show that the proposed algorithm outperforms several state-of-the-art algorithms in producing enhanced color constancy and contrast. The proposed algorithm also reduces noise effects and improves the details of an image without creating unnatural and inaccurate color constancy.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Agaian SS, Panetta KP, Grigoryan AM (2000) A new measure of image enhancement. Proc Int Conf Signal Process pp 19–22. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download? Accessed 15 September 2017

  2. Agaian SS, Silver B, Panetta KA (2007) Transform coefficient histogram-based image enhancement algorithms using contrast entropy. IEEE Trans Image Process 16:741–758. https://doi.org/10.1109/TIP.2006.888338

    Article  MathSciNet  Google Scholar 

  3. Agarwal V, Gribok AV, Abidi MA (2007) Machine learning approach to color constancy. Neural Netw 20:559–563. https://doi.org/10.1016/j.neunet.2007.02.004

    Article  MATH  Google Scholar 

  4. Bai XD, Cao ZG, Wang Y, Yu ZH, Zhang XF, Li CN (2013) Crop segmentation from images by morphology modeling in the CIE L*a*b* color space. Comput Electron Agric 99:21–34. https://doi.org/10.1016/j.compag.2013.08.022

    Article  Google Scholar 

  5. Banić N, Lončarić S (2017) Unsupervised Learning for Color Constancy. Proc 13th Int Jt Conf Comput Vision, Imaging Comput Graph Theory Appl 4:181–188. https://doi.org/10.5220/0006621801810188.

    Article  Google Scholar 

  6. Barnard K, Cardei V, Funt B (2002) A comparison of computational color constancy algorithms - Part I: Methodology and experiments with synthesized data. IEEE Trans Image Process 11:972–984. https://doi.org/10.1109/TIP.2002.802531

    Article  Google Scholar 

  7. Barnard K, Martin L, Coath A, Funt BV (2002) A comparison of computational color constancy algorithms--part II: experiments with image data. IEEE Trans Image Process 11:985–996. https://doi.org/10.1109/TIP.2002.802529

    Article  Google Scholar 

  8. Barnard K, Martin L, Funt B, Coath A (2002) A data set for color research. Color Res Appl 27:147–151. https://doi.org/10.1002/col.10049

    Article  Google Scholar 

  9. Bianco S, Ciocca G, Cusano C, Schettini R (2008) Classification-based color constancy, In: Vis. Inf. Syst. Web-Based Vis. Inf. Search Manag., Springer Berlin Heidelberg, pp. 104–113. https://doi.org/10.1007/978-3-540-85891-1_14

  10. Bianco S, Ciocca G, Cusano C, Schettini R (2010) Automatic color constancy algorithm selection and combination. Pattern Recogn 43:695–705. https://doi.org/10.1016/j.patcog.2009.08.007

    Article  MATH  Google Scholar 

  11. Bianco S, Cusano C, Schettini R (2015) Color constancy using CNNs. IEEE Comput Soc Conf Comput Vis Pattern Recognit Work:81–89. https://doi.org/10.1109/CVPRW.2015.7301275

  12. Buchsbaum G (1980) A spatial processor model for object colour perception. J Frankl Inst 310:1–26. https://doi.org/10.1016/0016-0032(80)90058-7.

    Article  Google Scholar 

  13. Cardei VC, Funt B, Barnard K (2002) Estimating the scene illumination chromaticity by using a neural network. J Opt Soc Am A 19:2374–2386

    Article  Google Scholar 

  14. Cepeda-Negrete J, Sanchez-Yanez RE (2015) Automatic selection of color constancy algorithms for dark image enhancement by fuzzy rule-based reasoning. Appl Soft Comput J 28:1–10. https://doi.org/10.1016/j.asoc.2014.11.034

    Article  Google Scholar 

  15. Faghih MM, Moghaddam ME (2014) Multi-objective optimization based color constancy. Appl Soft Comput J 17:52–66. https://doi.org/10.1016/j.asoc.2013.11.016

    Article  Google Scholar 

  16. 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. https://doi.org/10.1109/34.969113

    Article  Google Scholar 

  17. Finlayson GD, Trezzi E (2004) Shades of gray and colour constancy. Proc Twelfth Color Imaging Conf:37–41. https://doi.org/10.1353/hcr.0.0118

  18. Forsyth DA (1990) A Novel Algorithm for Color Constancy. Int J Comput Vis 5:5–36. https://doi.org/10.1007/BF00056770

    Article  Google Scholar 

  19. Funt BV, Barnard K, Martin L (1998) Is machine colour constancy good enough?, ECCV’98 Proc. 5th Eur. Conf. Comput. Vis. I. 445–459. https://doi.org/10.1007/BFb0055683

  20. Gasparini F, Schettini R (2004) Color balancing of digital photos using simple image statistics. Pattern Recogn 37:1201–1217. https://doi.org/10.1016/j.patcog.2003.12.007

    Article  Google Scholar 

  21. Ghani ASA, Isa NAM (2014) Underwater image quality enhancement through composition of dual-intensity images and Rayleigh-stretching. Springerplus 3:757. https://doi.org/10.1186/2193-1801-3-757

    Article  Google Scholar 

  22. Ghani ASA, Isa NAM (2015) Enhancement of low quality underwater image through integrated global and local contrast correction. Appl Soft Comput 37:332–344. https://doi.org/10.1016/j.asoc.2015.08.033

    Article  Google Scholar 

  23. Ghani ASA, Isa NAM (2015) Underwater image quality enhancement through integrated color model with Rayleigh distribution. Appl Soft Comput 27:219–230. https://doi.org/10.1016/j.asoc.2014.11.020

    Article  Google Scholar 

  24. Gijsenij A, Gevers T (2007) Color constancy by local averaging, Proc. - 14th Int. Conf. Image Anal. Process. Work. ICIAP 2007. 171–174. https://doi.org/10.1109/ICIAPW.2007.16

  25. Gijsenij A, Gevers T (2011) Color Constancy Using Natural Image Statistics and Scene Semantics. IEEE Trans Pattern Anal Mach Intell 33:687–698. https://doi.org/10.1109/Tpami.2010.93

    Article  Google Scholar 

  26. Gijsenij A, Gevers T, Van De Weijer J (2010) Generalized gamut mapping using image derivative structures for color constancy. Int J Comput Vis 86:127–139. https://doi.org/10.1007/s11263-008-0171-3

    Article  Google Scholar 

  27. Gijsenij A, Gevers T, Van De Weijer J (2011) Computational color constancy: Survey and experiments. IEEE Trans Image Process 20:2475–2489. https://doi.org/10.1109/TIP.2011.2118224

    Article  MathSciNet  MATH  Google Scholar 

  28. Gijsenij A, Lu R, Gevers T (2012) Color constancy for multiple light sources. IEEE Trans Image Process 21:697–707. https://doi.org/10.1109/TIP.2011.2165219

    Article  MathSciNet  MATH  Google Scholar 

  29. Hitam MS, Yussof WNJHW, Awalludin EA, Bachok Z (2013) Mixture Contrast Limited Adaptive Histogram Equalization for Underwater Image Enhancement. Int Conf Comput Appl Technol. ICCAT 2013:1–5. https://doi.org/10.1109/ICCAT.2013.6522017

  30. Hou L, Ji H, Shen Z (2013) Recovering Over-/Underexposed Regions in Photographs. SIAM J Imaging Sci 6:2213–2235. https://doi.org/10.1137/120888302

    Article  MathSciNet  MATH  Google Scholar 

  31. Hunt RWG (2005) The Reproduction of Colour, In: 6th edn. John Wiley & Sons, Ltd, Chichester UK, p 11–12. https://doi.org/10.1002/0470024275

  32. Jaya VL, Gopikakumari R (2013) IEM: A New Image Enhancement Metric for Contrast and Sharpness Measurements. Int J Comput Appl 79:1–9 http://research.ijcaonline.org/volume79/number9/pxc3891620.pdf

    Google Scholar 

  33. Jia-zheng Y, Li-yan T, Hong B, Jing-hua H, Rui-zhe Z (2009) llumination Estimation Combining Physical and Statistical Approaches, 2009 Third Int. Symp. Intell. Inf. Technol. Appl. 365–368. https://doi.org/10.1109/IITA.2009.86

  34. Katharine DGH, McGreevy M, Lipsitz SR, Linder JA, Rimm E (2009) Using Median Regression to Obtain Adjusted Estimates of Central Tendency for Skewed Laboratory and Epidemiologic Data. Clin Chem 55:165–169. https://doi.org/10.1373/clinchem.2008.106260

    Article  Google Scholar 

  35. 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 Intell 26:2356–2371. https://doi.org/10.1016/j.engappai.2013.07.023

    Article  Google Scholar 

  36. Kwok NM, Wang D, Jia X, Chen SY, Fang G, Ha QP (2011) Gray world based color correction and intensity preservation for image enhancement. Proc - 4th Int Congr Image Signal Process CISP 2011 2:994–998. https://doi.org/10.1109/CISP.2011.6100336

    Article  Google Scholar 

  37. Land EH (1977) The retinex theory of color vision. Sci Am 237:108–128. https://doi.org/10.1038/scientificamerican1277-108

    Article  Google Scholar 

  38. Land EH, McCann JJ (1971) Lightness and Retinex Theory. J Opt Soc Am 61(1):1–11

    Article  Google Scholar 

  39. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: Sensor-based activity recognition. Neurocomputing 181:108–115. https://doi.org/10.1016/j.neucom.2015.08.096

    Article  Google Scholar 

  40. Manikandan S (2011) Measures of central tendency: Median and mode. J Pharmacol Pharmacother 2:214. https://doi.org/10.4103/0976-500X.83300

    Article  Google Scholar 

  41. Mohd Jain Noordin MN, Mat Isa NA, Lim WH (2016) Saturation avoidance color correction for digital color images. Multimed Tools Appl. https://doi.org/10.1007/s11042-016-3620-y

  42. Montenegro J, Gomez W, Sanchez-Orellana P (2013) A comparative study of color spaces in skin-based face segmentation. 10th Int Conf Electr Eng Comput Sci Autom Control CCE 2013:313–317. https://doi.org/10.1109/ICEEE.2013.6676048

    Article  Google Scholar 

  43. Mutneja V, Behera DK (2014) Contrast enhancement analysis of video sequence in the temporal-based (TB) method. Int J Eng Educ 6:25–29

    Google Scholar 

  44. Naim MJNM, Isa NAM (2012) Pixel distribution shifting color correction for digital color images. Appl Soft Comput J 12:2948–2962. https://doi.org/10.1016/j.asoc.2012.04.028

    Article  Google Scholar 

  45. Naim MJNM, Isa NAM, Lim WH (2015) A new quantitative evaluation metric for color correction algorithm. Int Semin Intell Technol Its Appl 2015:213–218. https://doi.org/10.1109/ISITIA.2015.7219981

    Article  Google Scholar 

  46. Raimondo SS, Corchs (2010) Underwater image processing: State of the art of restoration and image enhancement methods. EURASIP J Adv Signal Process 2010:14. https://doi.org/10.1155/2010/746052

    Article  Google Scholar 

  47. Rani S, Kumar M (2014) Contrast Enhancement using Improved Adaptive Gamma Correction With Weighting Distribution Technique. Int J Comput Appl 101:47–53. https://doi.org/10.5120/17735-8849

    Article  Google Scholar 

  48. Recky M, Leberl F (2010) Windows detection using K-means in CIE-Lab color space. Proc - Int Conf Pattern Recognit:356–359. https://doi.org/10.1109/ICPR.2010.96

  49. Saravanan S, Siva Kumar P (2014) Image contrast enhancement using histogram equalization techniques: review. Int J Adv Comput Sci Technol 3:163–172

    Google Scholar 

  50. Stanikunas R, Vaitkevicius H, Kulikowski JJ (2004) Investigation of color constancy with a neural network. Neural Netw 17:327–337. https://doi.org/10.1016/j.neunet.2003.12.002

    Article  MATH  Google Scholar 

  51. Syahrir WM, Suryanti A, Connsynn C (2009) Color grading in Tomato Maturity Estimator using image processing technique. 2009 2nd IEEE Int Conf Comput Sci Inf Technol:276–280. https://doi.org/10.1109/ICCSIT.2009.5234497.

  52. Van De Weijer J, Gevers T (2005) Color Constancy based on the Grey-Edge Hypothesis, Proc. - Int. Conf. Image Process. ICIP. 2. 722–725. https://doi.org/10.1109/ICIP.2005.1530157

  53. Van De Weijer J, Gevers T, Gijsenij A (2007) Edge-based color constancy. IEEE Trans Image Process 16:2207–2214. https://doi.org/10.1109/TIP.2007.901808

    Article  MathSciNet  Google Scholar 

  54. Wirth M, Nikitenko D (2010) The effect of colour space on image sharpening algorithms. CRV 2010-7th Can Conf Comput Robot Vis:79–85. https://doi.org/10.1109/CRV.2010.17

  55. Wu J, Huang H, Qiu Y, Wu H, Tian J, Liu J (2005) Remote sensing image fusion based on average gradient of wavelet transform. In: IEEE Int. Conf. Mechatronics Autom., IEEE, Niagara Falls, Canada, pp. 1817–1821. https://doi.org/10.1109/ICMA.2005.1626836

  56. Zuiderveld K (1994) Contrast Limited Adaptive Histogram Equalization. In: Heckbert PS (ed) Graph. Gems IV, Academic Press Professional, Inc., San Diego, pp 474–485. https://doi.org/10.1016/B978-0-12-336156-1.50061-6.

    Chapter  Google Scholar 

Download references

Acknowledgments

The authors are grateful to the anonymous reviewers for their valuable comments and suggestions toward the improvement of this paper. The authors would also like to thank the Ministry of Higher Education of Malaysia for providing financial support under the Fundamental Research Grant Scheme for the study titled “Formulation of a Robust Framework of Image Enhancement for Non-uniform Illumination and Low-Contrast Images.”

Author information

Authors and Affiliations

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

    Wan Muhammad Syahrir Bin Wan Hussin, Mohd Naim Mohd Jain Noordin & Nor Ashidi Mat Isa

  2. Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300, Kuantan, Pahang, Malaysia

    Wan Muhammad Syahrir Bin Wan Hussin

Authors
  1. Wan Muhammad Syahrir Bin Wan Hussin
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Mohd Naim Mohd Jain Noordin
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Nor Ashidi Mat Isa
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Nor Ashidi Mat Isa.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix 1

Appendix 1

Fig. 12
figure 12figure 12

Resultant images of Mountain, Catching fish, Polar bears, Lake, and Releasing turtles after applying the proposed algorithm, SACC, DIRS-CLASH, PDSCC, GE1, GE2, GGW, and SG algorithms

Full size image
Fig. 13
figure 13figure 13

Resultant images of Library, Grass, Flower pot, Fence, and Snow after applying the proposed algorithm, SACC, DIRS-CLASH, PDSCC, GE1, GE2, GGW, and SG algorithms

Full size image
Fig. 14
figure 14figure 14

Resultant images of Bar, Laughing, Christmas tree, Toys, and Baby after applying the proposed algorithm, SACC, DIRS-CLASH, PDSCC, GE1, GE2, GGW, and SG algorithms

Full size image
Fig. 15
figure 15figure 15

Resultant images of Door, Walking, Fruits, Smiling, and Guy after applying the proposed algorithm, SACC, DIRS-CLASH, PDSCC, GE1, GE2, GGW, and SG algorithms

Full size image
Fig. 16
figure 16figure 16

Resultant images of Swimming fish, Holding breath, Underwater sand, Diving, and Striped fish after applying the proposed algorithm, SACC, DIRS-CLASH, PDSCC, GE1, GE2, GGW, and SG algorithms

Full size image
Fig. 17
figure 17figure 17

Resultant images of Coral branch, Fishes, Blue fish, Artificial reef, and A fish after applying the proposed algorithm, SACC, DIRS-CLASH, PDSCC, GE1, GE2, GGW, and SG

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hussin, W.M.S.B.W., Noordin, M.N.M.J. & Isa, N.A.M. Nonlinear local-pixel-shifting color constancy algorithm. Multimed Tools Appl 78, 10401–10448 (2019). https://doi.org/10.1007/s11042-018-6566-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6566-4

Keywords