Skip to main content
SpringerLink
Log in

A uniform illumination image enhancement via linear transformation in CIELAB color space

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

Abstract

Digital devices such as digital cameras and smartphones are important tools in capturing color images. However, images captured under a low-light condition have low contrast, low brightness, and they lose their color information. In this paper, we propose a uniform illumination image enhancement via linear transformation in CIELAB color space. The main objective of the proposed method is to preserve the image features and the hue of the enhanced images as close as possible to the original images. It is achieved by using a linear transformation of the chroma and lightness in the CIELAB color space. The proposed method consists of two main stages known as luminance and chrominance enhancements. The proposed method produces enhanced images having lesser distortion and higher similarity indexes than the other four enhancement methods.

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

Similar content being viewed by others

Data and Code Availability

The datasets and code generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Cai J, Gu S, Zhang L (2018) Learning a deep single image contrast enhancer from multi-exposure images. IEEE Trans Image Process 27(4):2049–2062

    Article  MathSciNet  Google Scholar 

  2. Dai Q, Pu YF, Rahman Z, Aamir M (2019) Fractional-order fusion model for low-light image enhancement. Symmetry 11(574):1–17

    Google Scholar 

  3. Dixit AK, Yadav RK (2019) A review on image contrast enhancement in colored images. Int J Comput Sci Eng 7(4):263–273

    Google Scholar 

  4. Dong X, Wang G, Pang Y, Li W, Wen J, Meng W, Liu Y (2011) Fast efficient algorithm for enhancement of low lighting video. In: International conference on multimedia and expo (ICME 2011)

  5. Fairchild MD (2005) Color appearance models. John Wiley & Sons Ltd

  6. Fairchild MD, Pirrotta E (1991) Predicting the lightness of chromatic object colors using cielab. Color Res Appl 16(6):385–393

    Article  Google Scholar 

  7. Gonzalez RC, Woods RE (2018) Digital Image Processing. 4th Edn, Pearson India

  8. Guo XJ, Li Y, Ling HB (2017) Lime: Low-light image enhancement via illumination map estimation. IEEE Trans Image Process 26(2):982–993

    Article  MathSciNet  Google Scholar 

  9. Jiang X, Yao H, Zhang S, Lu X, Zeng W (2013) Night video enhancement using improved dark channel prior. In: IEEE International conference on image processing, pp 553–557

  10. Kim D, Kim C (2017) Contrast enhancement using combined 1-d and 2-d histogram-based techniques. IEEE Signal Process Lett 24(6):804–808

    Article  Google Scholar 

  11. Ko S, Yu S, Park S, Moon B, Kang W, Paik J (2017) Variational framework for low-light image enhancement using optimal transmission map and combined l1 and l2-minimization. Signal Process Image Commun 58:99–110

    Article  Google Scholar 

  12. Land EH (1977) The retinex theory of color vision. Sci Am 237:108–129

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Li C, Guo J, Porikli F, Pang Y (2018) Lightennet: A convolutional neural network for weakly illuminated image enhancement. Pattern Recogn Lett 104:15–22

    Article  Google Scholar 

  15. Li G, Rana MN, Sun J, Song Y, Qu J (2020) Real-time image enhancement with efficient dynamic programming. Multimedia Tools and Applications

  16. Lore KG, Akintayo A, Sarkar S (2017) Llnet: A deep autoencoder approach to natural low-light image enhancement. Pattern Recogn 61:650–662

    Article  Google Scholar 

  17. Ma S, Ma H, Xu Y, Li S, Lv C, Zhu M (2018) A low-light sensor image enhancement algorithm based on hsi color model. Sensor 18(3583):1–16

    Google Scholar 

  18. Mandal S, Mitra S, Shankar BU (2020) Fuzzycie: Fuzzy colour image enhancement for low-exposure images. Soft Comput 24:2151–2167

    Article  Google Scholar 

  19. Nandal A, Bhaskar V, Dhaka A (2018) Contrast-based image enhancement algorithm using grey-scale and colour space. IET Signal Process 12:514–521

    Article  Google Scholar 

  20. Pascale D (2003) A review of RGB color spaces... from xyY to R’G’B’. The BabelColor Company, Montreal, Canada

  21. Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, Romeny BH, Zimmerman JB, Zuiderveld K (1987) Adaptive histogram equalization and its variations. Comput. Graph. Image Process. 39(3):355–368

    Article  Google Scholar 

  22. Ponomarenko N, Jin L, Ieremeiev O, Lukin V, Egiazarian K, Astola J, Vozel B, Chehdi K, Carli M, Battisti F, Kuo CCJ (2015) Image database tid2013: Peculiarities, results and perspectives. Signal Process Image Commun 30:57–77

    Article  Google Scholar 

  23. Sheikh HR, Bovik AC (2005) A visual information fidelity approach to video quality assessment. In: The first international workshop on video processing and quality metrics for consumer electronics, vol 7

  24. Shi Z, Feng Y, Zhao M, Zhang E, He L (2020) Normalised gamma transformation-based contrast-limited adaptive histogram equalisation with colour correction for sand-dust image enhancement. IET Image Process 14 (4):747–756

    Article  Google Scholar 

  25. Singh G, Khosla A, Anwar MI (2016) Spatial domain color image enhancement based on local processing. In: 3Rd international conference on signal processing and integrated networks (SPIN), pp 265–269

  26. Wang S, Zheng J, Hu HM, Li B (2013) Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Transaction Image Processing 22(9):3538–3548

    Article  Google Scholar 

  27. Wang Z, Bovik A, Sheikh H, Simoncelli E (2004) Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  28. Wu F, Kin TU (2017) Low-light image enhancement algorithm based on hsi color space. In: The 10th international congress on image and signal processing, biomedical engineering and informatics

  29. Yan L, Fu J, Wang C, Ye Z, Chen H, Ling H (2021) Enhanced network optimized generative adversarial network for image enhancement. Multimedia Tools and Application

  30. Zhang L, Shen P, Peng X, Zhu G, Song J, Wei W, Song H (2016) Simultaneous enhancement and noise reduction of a single low-light image. IET Image Process 10(11):840–847

    Article  Google Scholar 

  31. Zhang L, Zhang L, Mou X, Zhang D (2011) Fsim: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386

    Article  MathSciNet  Google Scholar 

  32. Zhang M, Zou F, Zheng J (2017) The linear transformation image enhancement algorithm based on hsv color space. Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 64:19–27

    Google Scholar 

  33. Zhang S, Wang T, Dong JY, Yu H (2017) Underwater image enhancement via extended multi-scale retinex. Neurocomputing 245:1–9

    Article  Google Scholar 

  34. Zuiderveld K (1994) Contrast limited adaptive histogram equalization. Academic Press Professional, Graphic Gems IV

Download references

Acknowledgments

The author is grateful for the constructive comments and suggestions from the anonymous reviewers on an earlier version of the paper.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, ornot-for-profit sectors.

Author information

Authors and Affiliations

  1. Institute of Computer Science and Digital Innovation, UCSI University, Kuala Lumpur, Malaysia

    Mohd Fikree Hassan

Authors
  1. Mohd Fikree Hassan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohd Fikree Hassan.

Ethics declarations

Conflict of Interests

The author certifies that he has no conflict of interest

Additional information

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hassan, M.F. A uniform illumination image enhancement via linear transformation in CIELAB color space. Multimed Tools Appl 81, 26331–26343 (2022). https://doi.org/10.1007/s11042-022-12429-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12429-7

Keywords

Search

Navigation