Underwater image enhancement using contrast limited adaptive histogram equalization and layered difference representation

Abstract

Underwater images, which have low contrast and visibility as a result of selective attenuation based on the wavelength of the light passing through water, needs some corrections to extract meaningful information from them. In this paper, we aim to combine two different approaches; global and local contrast enhancement techniques, to obtain better visual quality while enhancing image contrasts on underwater images. While global technique (LDR) ensures the overall enhancement of the image, local technique (CLAHE) considers local brightness features of the image in RGB color space. The proposed method also applies local color correction on underwater image. While methods in the literature apply various approaches on the global histogram of channels, our method divides underwater image into non-overlapping sub-blocks and apply histogram equalization on them. The method uses HSV color space and especially S, V components for color correction. The results of the qualitative analysis show that it produces very good images, in contrast, color, and detail compared to other enhancement methods. The proposed method also decreases the effect of under- and over-enhanced areas and the blue-green effect on the output image. However, the visibility of the objects in the images are increased by color correction. For quantitative analysis, the proposed method produces the highest average value of entropy (7.83), EMEE (32.06), EME (40.97), average gradient (152.55), and Sobel count (130393) for 200 underwater images.

This is a preview of subscription content, access via your institution.

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

References

  1. 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. 2.

    Abdul Ghani AS, Mat Isa NA (2015) Underwater image quality enhancement through integrated color model with Rayleigh distribution. Appl Soft Comput 27:219–230

    Article  Google Scholar 

  3. 3.

    Agaian SS, Lentz KP, Grigoryan AM (2000) A new measure of image enhancement. Proceedings of the International Conference of Signal Processing and Communication (IASTED), In, pp 19–22

    Google Scholar 

  4. 4.

    Bharal S (2015) L*a*b based contrast limited adaptive histogram equalization for underwater images. International Journal of Computer Application. Volume 5– No 4.

  5. 5.

    Çelebi AT, Ertürk S (2012) Visual enhancement of underwater images using empirical mode decomposition. Expert Syst Appl 39:800–805

    Article  Google Scholar 

  6. 6.

    Chen Y, Xu W, Zuo J, Yang K (2019) The fire recognition algorithm using dynamic feature fusion and IV-SVM classifier. Clust Comput 22:7665–7675

    Article  Google Scholar 

  7. 7.

    Chen Y, Wang J, Liu S, Chen X, Xiong J, Xie J, Yang K (2019 online) Multiscale fast correlation filtering tracking algorithm based on a feature fusion model. Concurrency and Computation-Practice and Experience. https://doi.org/10.1002/cpe.5533

  8. 8.

    Chen Y, Tao J, Zhang Q, Yang K, Chen X, Xiong J, Xia R, Xie J (2020) Saliency detection via improved hierarchical principle component analysis method. Wireless Communications and Mobile Computing:Article ID 8822777, 2020. https://doi.org/10.1155/2020/8822777

  9. 9.

    Chen Y, Tao J, Liu L, Xiong J, Xia R, Xie J, Zhang Q, Yang K Research of improving semantic image segmentation based on a feature fusion model. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02066-z

  10. 10.

    Chiang JY, Chen YC (2012) Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans Image Process 21:1756–1769

    MathSciNet  Article  Google Scholar 

  11. 11.

    Cong R, Lei J, Fu H, Cheng M, Lin W (2018) Huang Q (2018) Review of visual saliency detection with comprehensive information. IEEE Trans Circuits Syst Video Technol

  12. 12.

    Eustice R, Pizarro O, Singh H, Howland J (2002) UWIT: Underwater image toolbox for optical image processing and mosaicking in MATLAB. In: Proceedings of the 2002 International Symposium on Underwater Technology. Tokyo, Japan, pp 141–145

    Google Scholar 

  13. 13.

    Deng-Ping Fan, Ge-Peng Ji, Guolei Sun, Ming-Ming Cheng, Jianbing Shen, Ling Shao (2020) Camouflaged object detection. In IEEE CVPR.

  14. 14.

    Galdran A, Pardo D, Picón A, Alvarez-Gila A (2015) Automatic red-channel underwater image restoration. J Vis Commun Image Represent 26:132–145

    Article  Google Scholar 

  15. 15.

    Garg D, Garg NK, Kumar M (2018) Underwater image enhancement using blending of CLAHE and percentile methodologies. Multimed Tools Appl 77(20):26545–26561

    Article  Google Scholar 

  16. 16.

    Ghani ASA, Isa NAM (2017) Automatic system for improving underwater image contrast and color through recursive adaptive histogram modification. Comput Electron Agric 141:181–195

    Article  Google Scholar 

  17. 17.

    Gordon H (1989) Theoretical aspects of hydrologic optics. Limnol Oceanogr 34:1389–1409

    Article  Google Scholar 

  18. 18.

    Hitam MS, Yussof WNJW, Awalludin EA, Bachok Z (2013) Mixture contrast limited adaptive histogram equalization for underwater image enhancement. IEEE International Conference on Computer Applications Technology (ICCAT), pp. 1–5.

  19. 19.

    Iqbal K, Abdul Salam R, Osman M, Talib AZ (2007) Underwater image enhancement using An integrated colour model. IAENG Int J Comput Sci 32:239–244

    Google Scholar 

  20. 20.

    Iqbal K, Odetayo M, James A, Salam RA, Talib AZH (2010)Enhancing the low quality images using unsupervised color correction method. International Conference on System Man and Cybernetics (SMC), 10–13 October, Istanbul, pp. 1703–1709.

  21. 21.

    Le TN, Le YT, Tran MT (2014) Applying saliency-based region of interest detection in developing a collaborative active learning system with augmented reality. International Conference on Virtual, Augmented and Mixed Reality, In, pp 51–62

    Google Scholar 

  22. 22.

    Le T-N, Nguyen T, Nie Z, Tran M-T (2019) Akihiro Sugimoto. Anabranch Network for Camouflaged Object Segmentation, Journal of Computer Vision and Image Understanding (CVIU)

    Google Scholar 

  23. 23.

    Lee C, Kim CS (2013) Contrast enhancement based on layered difference representation of 2d histograms. Image Process IEEE Trans 22(12):5372–5384

    Article  Google Scholar 

  24. 24.

    Li C, Guo J, Guo C, Cong R, Gong J (2017a) A hybrid method for underwater image correction. Pattern Recogn Lett 94:62–67

  25. 25.

    Li L, Wang H, Liu X (2017b) Underwater image enhancement based on improved dark priori and color correction. J Opt 37(12):1211003

  26. 26.

    Li C, Guo J, Guo C (2018) Emerging from water: underwater image color correction based on weakly supervised color transfer. IEEE Signal Processing Letters 25(3):323–327

    Article  Google Scholar 

  27. 27.

    Li C, Guo C, Ren W, Cong R, Hou J, Kwong S, Tao D (2019) An underwater image enhancement benchmark dataset and beyond. CoRR, abs/1901.05495.

  28. 28.

    Li C, Anwar S, Porikli F (2020) Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recogn 98:107038–107049

    Article  Google Scholar 

  29. 29.

    Lu W, Zhang X, Lu H, Li F (August 2020) Deep hierarchical encoding model for sentence semantic matching. J Vis Commun Image Represent 71:102794. https://doi.org/10.1016/j.jvcir.2020.102794

    Article  Google Scholar 

  30. 30.

    Luo Y, Qin J, Xiang X, Tan Y, Liu Q, Xiang L (2020) Coverless real-time image information hiding based on image block matching and Dense Convolutional Network. J Real-Time Image Proc 17(1):125–135

    Article  Google Scholar 

  31. 31.

    Naim MJNM, Isa NAM (2012) Pixel distribution shifting color correction for digital color images. J Appl Soft Comp 12(9):2948–2962

    Article  Google Scholar 

  32. 32.

    Qiao X, Bao J, Zhang H, Zeng L, Li D (2017) Underwater image quality enhancement of sea cucumbers based on improved histogram equalization and wavelet transform. Inf Process Agric 4(3):206–213

    Google Scholar 

  33. 33.

    Sun L, Ma C, Chen Y, Zheng Y, Shim HJ, Wu Z, Jeon B (October 2019, online) Low rank component induced spatial-spectral kernel method for hyperspectral image classification. IEEE Transactions on Circuits and Systems for Video Technology. https://doi.org/10.1109/TCSVT.2019.2946723

  34. 34.

    Sun L, Wu F, Zhan T, Liu W, Wang J, Jeon B (2020) Weighted Nonlocal Low-Rank Tensor Decomposition Method for Sparse Unmixing of Hyperspectral Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13:1174–1188. https://doi.org/10.1109/JSTARS.2020.2980576

    Article  Google Scholar 

  35. 35.

    Vasamsetti S, Mittal N, Neelapu BC, Sardana HK (2017) Wavelet based perspective on variational enhancement technique for underwater imagery. Ocean Eng 141:88–100

    Article  Google Scholar 

  36. 36.

    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: Proceeding of the IEEE International Conference on Mechatronics & Automation, , Niagara Falls, Canada, vol 4, pp 1817–1821

    Google Scholar 

  37. 37.

    Ye Z (2009) Objective assessment of nonlinear segmentation approaches to gray level underwater images. Int J Graph Vis Image Process (GVIP) 9(II):39–46

    Google Scholar 

  38. 38.

    Yin X, Zhang M, Wang L, Liu Y (2020) Interface debonding performance of precast segmental nano-materials based concrete (PSNBC) beams. Mater Express 10:1317–1327

    Article  Google Scholar 

  39. 39.

    Zhang J, Xie Z, Sun J, Zou X, Wang J (2020) A cascaded R-CNN with multiscale attention and imbalanced samples for traffic sign detection. IEEE Access 8:29742–29754. https://doi.org/10.1109/ACCESS.2020.2972338

    Article  Google Scholar 

  40. 40.

    Yuteng Zhang, Wenpeng Lu, Weihua Ou, Guoqiang Zhang, Xu Zhang, Jinyong Cheng, Weiyu Zhang. Chinese medical question answer selection via hybrid models based on CNN and GRU. Multimed Tools Appl, volume 79, pages14751–14776 (2020).

  41. 41.

    Zhao JX, Liu J, Fan DP, Cao Y, Yang J, Cheng M (2019) Egnet: Edge guidance network for salient object detection. in Proc IEEE Int Conf Comput Vis, pp. 8779–8788.

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Beste Ustubioglu.

Additional information

Publisher’s note

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

Appendix

Appendix

figurea
Image   Original HE ICM UCM CLAHS CLAHE–Mix CLAHE and Percentile ICMRD RAHIM Proposed
Image 1 Entropy 7,23 5,98 7,63 7,26 7,36 7,52 7,56 7,88 7,67 7,88
EME 9,10 23,20 25,30 24,35 16,43 21,40 22,43 25,58 24,85 47,76
EMEE 0,57 4,94 6,04 4,69 1,47 2,47 2,73 9,53 5,27 41,91
Gradient 51,93 121,81 96,41 79,28 99,29 104.25 100,75 109,583 96,22 169,87
Sobel 14065 34114 29294 24623 32357 33557 32136 32694 31164 43952
Image 2 Entropy 6,12 5,49 7,04 6,77 6,27 6,92 6,76 7,25 7,52 7,78
EME 2,77 23,86 13,73 12,01 5,20 7,41 5,61 16,32 14,66 31,65
EMEE 0,13 5,37 2,16 1,48 0,28 0,47 0,33 5,66 1,72 10,06
Gradient 8,95 82,30 39,96 38,14 19,61 37,20 24,34 45 43,92 87,29
Sobel 39 24143 5108 3598 430 3547 1017 6200 5799 26228
Image 3 Entropy 7,43 5,98 7,68 7,22 7,33 7,53 7,65 7,88 7,70 7,89
EME 8,88 22,83 25,72 24,06 15,53 21,38 23,15 22,78 25,25 47,88
EMEE 0,55 4,62 5,70 4,35 1,35 2,59 2,84 7,69 6,17 45,87
Gradient 45 100,30 83,42 63,71 90,93 101,89 92,68 90,82 85,83 167,74
Sobel 11211 30713 25552 18913 29686 32751 29472 29005 27055 45534
Image 4 Entropy 7,16 5,95 7,26 6,79 7,46 7,65 7,49 7,87 7,64 7,87
EME 17,84 25,76 34,45 39,23 20,20 30,91 26,70 31,10 33,96 41
EMEE 1,72 7,35 14,36 17,43 2,37 6,38 3,84 12,53 11,71 33,05
Gradient 62,79 129,20 101,82 76,50 99,16 107,95 105,13 120,54 109,42 154
Sobel 14317 31233 22876 18315 25703 27156 26061 30167 27295 34603

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ulutas, G., Ustubioglu, B. Underwater image enhancement using contrast limited adaptive histogram equalization and layered difference representation. Multimed Tools Appl (2021). https://doi.org/10.1007/s11042-020-10426-2

Download citation

Keywords

  • CLAHE
  • LDR
  • Contrast correction
  • Color correction