Skip to main content
SpringerLink
Log in

Color balance and sand-dust image enhancement in lab space

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

Abstract

Due to the scattering and absorption of light, the captured image under sand-dust weather has serious color shift and poor visibility, which can affect the application of computer vision. To solve those problems, the present study proposes a color balance and sand-dust image enhancement algorithm in Lab space. To correct the color of the sand-dust image, a color balance technique is put forward. At first, the color balance technique employs the green channel to compensate the lost value of the blue channel. Then, the technique based on statistical strategy is employed to remove the color shift. The proposed color balance technique can effectively remove the color shift while reducing the blue artifact. The brightness component L is decomposed by guided filtering to obtain the detail component. In the meanwhile, to enhance the detail information of the image, the nonlinear mapping function and gamma function are introduced to the detail component. Experimental results based on qualitative and quantitative evaluation demonstrate that the proposed method can effectively remove color shift, enhance details and contrast of the image and produce results superior to those of other state-of-the-art methods. Additionally, the proposed algorithm can satisfy real-time applications, which can also be used to restore images of turbid underwater and haze 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

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. Afifi M, Price B, Cohen S, Brown MS (2019) When color constancy goes wrong: correcting improperly white-balanced images. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 1535–1544

    Google Scholar 

  2. Al-Ameen Z (2016) Visibility enhancement for images captured in dusty weather via tuned tri-threshold fuzzy intensification operators. International Journal of Intelligent Systems and Applications 8(8):10–17

    Article  Google Scholar 

  3. Al-Sammaraie MF (2015) Contrast enhancement of roads images with foggy scenes based on histogram equalization. In: 2015 10th international conference on computer science & education (ICCSE), pp 95–101

  4. Ancuti CO, Ancuti C, De Vleeschouwer C, Bekaert P (2018) Color balance and fusion for underwater image enhancement. IEEE Trans Image Process 27(1):379–393

    Article  MathSciNet  Google Scholar 

  5. Berman D, Avidan S et al (2016) Non-local image dehazing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1674–1682

    Google Scholar 

  6. Berman D, Treibitz T, Avidan S (2020) Single Image Dehazing Using Haze-Lines. IEEE Trans Pattern Anal Mach Intell 42(2):720–734

    Article  Google Scholar 

  7. Cho Y, Jeong J, Kim A (2018) Model-assisted multiband fusion for single image enhancement and applications to robot vision. IEEE Robot Autom Lett 3(4):2822–2829

    Google Scholar 

  8. Dhara SK, Roy M, Sen D, Biswas PK (2020) Color cast dependent image Dehazing via adaptive Airlight refinement and non-linear color balancing. IEEE Trans Circuits Syst Video Technol 31(5):1–5

    Google Scholar 

  9. Fu X, Huang Y, Zeng D et al (2014) A fusion-based enhancing approach for single sandstorm image. In: 2014 IEEE 16th international workshop on multimedia signal processing (MMSP). IEEE, pp 1–5

    Google Scholar 

  10. Gao G, Lai H, Jia Z, Liu YQ, Wang YL (2020) Sand-dust image restoration based on reversing the Blue Channel prior. IEEE Photonics J 12(2):1–16

    Google Scholar 

  11. Gao GX, Lai HC, Liu YQ, Wang LJ, Jia ZH (2021) Sandstorm image enhancement based on YUV space. Optik (Stuttg) 226:165659

    Article  Google Scholar 

  12. Hautière N, Tarel JP, Aubert D, Dumont É (2008) Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal Stereol 27(2):87–95

    Article  MathSciNet  Google Scholar 

  13. He K, Sun J, Tang X (2011) Single image haze removal using Dark Channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353

    Article  Google Scholar 

  14. He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409

    Article  Google Scholar 

  15. Huo JY, Chang YL, Wang J, Wei XX (2006) Robust automatic white balance algorithm using gray color points in images. IEEE Trans Consum Electron 52(2):541–546

    Article  Google Scholar 

  16. Kim SE, Park TH, Eom IK (2020) Fast single image Dehazing using saturation based transmission map estimation. IEEE Trans Image Process 29:1985–1998

    Article  MathSciNet  Google Scholar 

  17. Koscevic K, Subasic M, Loncaric S (2019) Attention-based convolutional neural network for computer vision color constancy. In: Int Symp image signal process anal ISPA, pp 372–377

    Google Scholar 

  18. Li B, Peng X, Wang Z et al (2017) AOD-Net: All-in-One Dehazing Network. In: Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, pp 4770–4778

  19. Li M, Liu J, Yang W, Sun X, Guo Z (2018) Structure-revealing low-light image enhancement via robust Retinex model. IEEE Trans Image Process 27(6):2828–2841

    Article  MathSciNet  Google Scholar 

  20. Ning X, Li W, Liu W (2017) A fast single image haze removal method based on human retina property. IEICE Trans Inf Syst E100(1):211–214

    Article  Google Scholar 

  21. Ning X, Gong K, Li W, Zhang L, Bai X, Tian S (2020) Feature refinement and filter network for person re-identification. IEEE Trans Circuits Syst Video Technol 31:3391–3402

    Article  Google Scholar 

  22. Ou FZ, Wang YG, Zhu G (2019) A novel blind image quality assessment method based on refined natural scene statistics. In: 2019 IEEE International Conference on Image Processing (ICIP), pp 1004–1008

    Chapter  Google Scholar 

  23. Peng Y-T, Cao K, Cosman PC (2018) Generalization of the Dark Channel prior for single image restoration. IEEE Trans Image Process 27(6):2856–2868

    Article  MathSciNet  Google Scholar 

  24. Qin X, Wang Z, Bai Y et al (2020) FFA-net: feature fusion attention network for single image dehazing. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 11908–11915

    Google Scholar 

  25. Rong Z, Jun WL (2014) Improved wavelet transform algorithm for single image dehazing. Optik (Stuttg) 125(13):3064–3066

    Article  Google Scholar 

  26. Shi Z, Feng Y, Zhao M, Zhang E, He L (2019) Let you see in sand dust weather: a method based on halo-reduced Dark Channel prior Dehazing for sand-dust image enhancement. IEEE Access 7:116722–116733

    Article  Google Scholar 

  27. Tai SC, Liao TW, Chang YY, Yeh CP (2012) Automatic white balance algorithm through the average equalization and threshold. In: 2012 8th international conference on information science and digital content technology (ICIDT2012) 3, pp 571–576

    Google Scholar 

  28. Talebi H, Milanfar P (2016) Fast multilayer Laplacian enhancement. IEEE Trans Comput Imaging 2(4):496–509

    Article  MathSciNet  Google Scholar 

  29. Wang A, Wang W, Liu J, Gu N (2019) AIPNet: image-to-image single image Dehazing with atmospheric illumination prior. IEEE Trans Image Process 28(1):381–393

    Article  MathSciNet  Google Scholar 

  30. Yang X, Xu Z, Luo J (2018) Towards perceptual image dehazing by physics-based disentanglement and adversarial training. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 7485–7492

    Google Scholar 

  31. Yang Y, Zhang C, Liu L, Chen G, Yue H (2020) Visibility restoration of single image captured in dust and haze weather conditions. Multidimens Syst Signal Process 31(2):619–633

    Article  Google Scholar 

  32. Yu S, Hong Z, Jing W et al (2016) Single sand-dust image restoration using information loss constraint. Opt Acta Int J Opt 63(21):2121–2130

    Google Scholar 

  33. Zhang J, Tao D (2019) FAMED-net: a fast and accurate multi-scale end-to-end Dehazing network. IEEE Trans Image Process 29:72–84

    Article  MathSciNet  Google Scholar 

  34. Zhu Q, Mai J, Shao L (2015) A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process 24(11):3522–3533

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China

    GuXue Gao, HuiCheng Lai, LieJue Wang & ZhenHong Jia

  2. Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi, 830046, China

    GuXue Gao, HuiCheng Lai, LieJue Wang & ZhenHong Jia

Authors
  1. GuXue Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. HuiCheng Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. LieJue Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. ZhenHong Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to HuiCheng Lai.

Additional information

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

Gao, G., Lai, H., Wang, L. et al. Color balance and sand-dust image enhancement in lab space. Multimed Tools Appl 81, 15349–15365 (2022). https://doi.org/10.1007/s11042-022-12276-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12276-6

Keywords

Search

Navigation