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Underwater image enhancement based on adaptive color correction and multi-scale fusion

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Abstract

In actual captured underwater images, serious color distortion, detail loss and limited visibility are common issues due to the combined influence of water medium scattering and absorption. To solve these problems, an underwater image enhancement method based on adaptive color correction and multi-scale fusion is proposed, which does not rely on any specific equipment or auxiliary information. According to the attenuation degree of underwater images, an adaptive color correction method of piecewise linear transformation is used to address the color distortion. Then, the color-corrected image is converted from RGB to CIE-Lab color space, and the luminance channel L is applied to the updated logarithmic image processing (LIP) model alone to improve contrast. The final enhanced image is created by fusing the color-corrected image with the contrast-improved image using a multi-scale fusion technique based on contrast, saliency, and saturation. The qualitative and quantitative evaluation results demonstrate that compared with some state-of-the-art methods, the proposed method can produce better visual quality and have higher average values in underwater image quality evaluation such as AG, IE, PCQI, UIQM and UCIQE.

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References

  1. Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp: 1597-1604

  2. Ancuti CO, Ancuti C (2013) Single image dehazing by multi-scale fusion. IEEE Trans Image Process 22(8):3271–3282

    Google Scholar 

  3. Ancuti C, Ancuti CO, Haber T, Bekaert P (2012) Enhancing underwater images and videos by fusion. IEEE Conference Comput Vis Pattern Recog 2012:81–88

    Google Scholar 

  4. Ancuti CO, Ancuti C, De Vleeschouwer C, Bekaert P (2017) Color balance and fusion for underwater image enhancement. IEEE Trans Image Process 27(1):379–393

    MathSciNet  Google Scholar 

  5. Ancuti CO, Ancuti C, Vleeschouwer CD, Garcia R (2017) Locally Adaptive Color Correction for Underwater Image Dehazing and Matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp: 1-9

  6. Anwar S, Li C (2020) Diving deeper into underwater image enhancement: A survey. Signal Process: Image Commun 89:115978

    Google Scholar 

  7. Ao J, Ma C (2018) Adaptive stretching method for underwater image color correction. Int J Pattern Recogni Artificial Intell 32(2):1854001

    MathSciNet  Google Scholar 

  8. Bai L, Zhang W, Pan X, Zhao C (2020) Underwater image enhancement based on global and local equalization of histogram and dual-image multi-scale fusion. IEEE Access 8:128973–128990

    Google Scholar 

  9. Berman D, Levy D, Avidan S, Treibitz T (2020) Underwater single image color restoration using haze-lines and a new quantitative dataset. IEEE Trans Pattern Anal Machine Intell 43(8):2822–2837

    Google Scholar 

  10. Bhateja V, Misra M, Urooj S (2020) Human Visual System Based Unsharp Masking for Enhancement of Mammograms. Non-Linear Filters for Mammogram Enhancement, In, pp 199–222

    Google Scholar 

  11. Boom BJ, He J, Palazzo S, Huang PX, Fisher RB (2014) A research tool for long-term and continuous analysis of fish assemblage in coral-reefs using underwater camera footage. Ecol Inform 23(9):83–97

    Google Scholar 

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

    Google Scholar 

  13. Burt PJ, Adelson EH (1987) The Laplacian pyramid as a compact image code. Readings Comput Vision 31(4):671–679

    Google Scholar 

  14. Chiang JY, Chen YC (2011) Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans Image Process 21(4):1756–1769

    MathSciNet  Google Scholar 

  15. Drews P, Nascimento E, Moraes F, Botelho S, Campos M (2013) Transmission estimation in underwater single images. Proc IEEE Int Conf Comput Vis Workshops 2013:825–830

    Google Scholar 

  16. Finlayson GD, Trezzi E (2004) Shades of gray and colour constancy. In Color and Imaging Conference. Soc Imaging Sci Technol 2004(1):37–41

    Google Scholar 

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

    Google Scholar 

  18. Gao F, Wang K, Yang Z, Wang Y, Zhang Q (2021) Underwater image enhancement based on local contrast correction and multi-scale fusion. J Marine Sci Eng 9(2):225

    Google Scholar 

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

    Google Scholar 

  20. He K, Sun J, Tang X (2011) Single Image Haze Removal Using Dark Channel Prior. IEEE Trans Pattern Anal Machine Intell 33(12):2341–2353

    Google Scholar 

  21. Huang D, Wang Y, Song W, Sequeira J, Mavromatis S (2018) Shallow-water image enhancement using relative global histogram stretching based on adaptive parameter acquisition. Proc Int Conf Multimedia Model 10704:453–465

    Google Scholar 

  22. Hummel R (1977) Image enhancement by histogram transformation. Comput Graphics Image Process 6(2):184–195

    Google Scholar 

  23. Iqbal K, Odetayo M, James A, Salam RA, Talib AZH (2010) Enhancing the low quality images using unsupervised colour correction method. In: Proceedings of 2010 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp: 1703-1709

  24. Islam MJ, Xia Y, Sattar J (2020) Fast underwater image enhancement for improved visual perception. IEEE Robotics Autom Lett 5(2):3227–3234

    Google Scholar 

  25. Jaffe JS (1990) Computer modeling and the design of optimal underwater imaging systems. IEEE J Ocean Eng 15(2):101–111

    Google Scholar 

  26. Kashif I, Salam RA, Azam O, Talib AZ (2007) Underwater image enhancement using an integrated colour model. Iaeng Int J Comput Sci 34(2):239–244

    Google Scholar 

  27. Khan A, Ali SSA, Anwer A, Adil SH, Meriaudeau F (2018) Subsea Pipeline Corrosion Estimation by Restoring and Enhancing Degraded Underwater Images. IEEE Access 6:40585–40601

    Google Scholar 

  28. Land EH (1977) The retinex theory of color vision. Sci Am 237(6):108–129

    Google Scholar 

  29. Li C, Guo J, Cong R, Pang Y, Wang B (2016) Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior. IEEE Trans Image Process 25(12):5664–5677

    MathSciNet  Google Scholar 

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

    Google Scholar 

  31. Li C, Guo C, Ren W, Cong R, Hou J, Kwong S, Tao D (2019) An underwater image enhancement benchmark dataset and beyond. IEEE Trans Image Process 29:4376–4389

    Google Scholar 

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

    Google Scholar 

  33. Li C, Anwar S, Hou J, Cong R, Guo C, Ren W (2021) Underwater image enhancement via medium transmission-guided multi-color space embedding. IEEE Trans Image Process 30:4985–5000

    Google Scholar 

  34. Liang Z, Wang Y, Ding X, Mi Z, Fu X (2021) Single underwater image enhancement by attenuation map guided color correction and detail preserved dehazing. Neurocomputing 425:160–172

    Google Scholar 

  35. Liu R, Fan X, Zhu M, Hou M, Luo Z (2020) Real-world underwater enhancement: Challenges, benchmarks, and solutions under natural light. IEEE Trans Circuits Syst Video Technol 30(12):4861–4875

    Google Scholar 

  36. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110

    Google Scholar 

  37. Lu H, Li Y, Zhang Y, Chen M, Serikawa S, Kim H (2017) Underwater optical image processing: a comprehensive review. Mobile Networks Appl 22(6):1204–1211

    Google Scholar 

  38. Marques TP, Albu AB (2020) L2uwe: A framework for the efficient enhancement of low-light underwater images using local contrast and multi-scale fusion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp: 538-539

  39. Moghimi MK, Mohanna F (2021) Real-time underwater image enhancement: a systematic review. J Real-Time Image Process 18(5):1509–1525

    Google Scholar 

  40. Naik A, Swarnakar A, Mittal K (2021) Shallow-uwnet: Compressed model for underwater image enhancement (student abstract). Proc AAAI Confer Artificial Intell 35(18):15853–15854

    Google Scholar 

  41. Narasimhan SG, Nayar SK (2003) Contrast restoration of weather degraded images. IEEE Trans Pattern Anal Machine Intell 25(6):713–724

    Google Scholar 

  42. Pan PW, Yuan F, Cheng E (2018) Underwater image de-scattering and enhancing using dehazenet and HWD. J Marine Sci Technol 26(4):531–540

    Google Scholar 

  43. Panetta K, Gao C, Agaian S (2015) Human-visual-system-inspired underwater image quality measures. IEEE J Ocean Eng 41(3):541–551

    Google Scholar 

  44. Peng YT, Cosman PC (2017) Underwater image restoration based on image blurriness and light absorption. IEEE Trans Image Process 26(4):1579–1594

    MathSciNet  Google Scholar 

  45. Raveendran S, Patil MD, Birajdar GK (2021) Underwater image enhancement: a comprehensive review, recent trends, challenges and applications. Artificial Intell Rev 54(7):5413–5467

    Google Scholar 

  46. Schechner YY, Karpel N (2005) Recovery of underwater visibility and structure by polarization analysis. IEEE J Ocean Eng 30(3):570–587

    Google Scholar 

  47. Serikawa S, Lu H (2014) Underwater image dehazing using joint trilateral filter. Comput Electric Eng 40(1):41–50

    Google Scholar 

  48. Song W, Wang Y, Huang D, Tjondronegoro D (2018) A rapid scene depth estimation model based on underwater light attenuation prior for underwater image restoration. In: Pacific Rim Conference on Multimedia, pp: 678-688

  49. Steffens C, Drews PLJ, Botelho SS (2018) Deep Learning Based Exposure Correction for Image Exposure Correction with Application in Computer Vision for Robotics. In: Proceedings of 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE), pp: 6–10

  50. Van De Weijer J, Gevers T, Gijsenij A (2007) Edge-based color constancy. IEEE Trans Image Process 16(9):2207–2214

    MathSciNet  Google Scholar 

  51. Wang S, Ma K, Yeganeh H, Wang Z, Lin W (2015) A patch-structure representation method for quality assessment of contrast changed images. IEEE Signal Process Lett 22(12):2387–2390

    Google Scholar 

  52. Wang Y, Zhang J, Cao Y, Wang Z (2017) A deep CNN method for underwater image enhancement. In: Proceedings of IEEE International Conference on Image Processing (ICIP), pp: 1382-1386

  53. Wang Y, Song W, Fortino G, Qi LZ, Zhang W, Liotta A (2019) An experimental-based review of image enhancement and image restoration methods for underwater imaging. IEEE Access 7:140233–140251

    Google Scholar 

  54. Wang N, Zhou Y, Han F, Zhu H, Yao J (2019) UWGAN: Underwater GAN for real-world underwater color restoration and dehazing. arXiv preprint arXiv:1912.10269

  55. Wang W, Wu X, Yuan X, Gao Z (2020) An experiment-based review of low-light image enhancement methods. IEEE Access 8:87884–87917

    Google Scholar 

  56. Xue X, Hao Z, Ma L, Wang Y, Liu R (2021) Joint luminance and chrominance learning for underwater image enhancement. IEEE Signal Process Lett 28:818–822

    Google Scholar 

  57. Yang M, Sowmya A (2015) An underwater color image quality evaluation metric. IEEE Trans Image Process 24(12):6062–6071

    MathSciNet  Google Scholar 

  58. Zhang W, Dong L, Pan X, Zhou J, Qin L, Xu W (2019) Single image defogging based on multi-channel convolutional MSRCR. IEEE Access 7(1):72492–72504

    Google Scholar 

  59. Zhang W, Dong L, Pan X, Zou P, Qin L, Xu W (2019) A survey of restoration and enhancement for underwater images. IEEE Access 7:182259–182279

    Google Scholar 

  60. Zhang W, Dong L, Zhang T, Xu W (2021) Enhancing underwater image via color correction and Bi-interval contrast enhancement. Signal Process Image Commun 90:116030

    Google Scholar 

  61. Zuiderveld K (1994) Contrast limited adaptive histogram equalization. Graphics Gems 1994:474–485

    Google Scholar 

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Acknowledgments

This work was supported by the National Natural Science Foundation of China No. 62103072, China Postdoctoral Science Foundation No. 2021M690502 and the Fundamental Research Funds for the Central Universities No. 3132021242.

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  1. Dalian Maritime University, DaLian, China

    Jinyu Shi, Shanshan Yu, Huanan Li, Xiuguo Zhang & Changxin Liu

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  1. Jinyu Shi
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Shi, J., Yu, S., Li, H. et al. Underwater image enhancement based on adaptive color correction and multi-scale fusion. Multimed Tools Appl 83, 12535–12559 (2024). https://doi.org/10.1007/s11042-023-15652-y

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