Skip to main content
SpringerLink
Log in

A hybrid algorithm for underwater image restoration based on color correction and image sharpening

  • Special Issue Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

In this paper, an effective method is proposed for restoring underwater image based on color correction and image sharpening. The main purpose of this method is to improve the visibility of underwater images. Traditional methods generally adopt color balancing method to restore the images. However, we found that color balancing has a poor effectiveness on underwater image when the values of blue channel are greater than other two channels, specially, red channel is great of small than blue channel. Therefore, we propose a hybrid method for color correction process based on a principle that exploits the relationship of three channels (red channel, green channel, and blue channel). On the other words, color balancing will be employed to restore the images when the values of red channel approximate to blue channel, while the DCP-base method will be used if otherwise. To enhance the sharpness of underwater image, we employed a sharpening process based on Maximum a Posteriori (MAP) method when color correction has finished. The proposed method also been validated through carrying out experiments on several underwater image datasets which are provided by previous researchers. Our validation has proved that the proposed method has a better performance than the state-of-the-art 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
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Kannan, S.: Intelligent object recognition in underwater images using evolutionary-based Gaussian mixture model and shape matching. Signal Image Video Process. 2020, 877–885 (2020)

    Article  Google Scholar 

  2. José, P.S., Bruno, M.F., Nuno, A.C.: Guidance of an autonomous surface vehicle for underwater navigation Aid. In: 2018 IEEE/OES autonomous underwater vehicle workshop (AUV), (2018)

  3. Levedahl, B.A., Silverberg, L.: Control of underwater vehicles in full unsteady flow. IEEE J. Ocean. Eng. 34(4), 656–668 (2009)

    Article  Google Scholar 

  4. Prabowo, M.R., Hudayani, N., Purwiyanti, S., Sulistiyanti, S.R., Setyawan, F.X.A.: A moving objects detection in underwater video usingsubtraction of the background model. EECSI, pp. 19–21 (2017).https://doi.org/10.1109/EECSI.2017.8239148

  5. Vasamsetti, S., Setia, S., Mittal, N., Sardana, H.K., Babbar, G.: Automatic underwater moving object detection using multi-feature integration framework in complex backgrounds. IET Comput. Vis. 2, 770–778 (2018)

    Article  Google Scholar 

  6. Mazel, C.H.: In situ measurement of reflectance and fluorescence spectra to support hyperspectral remote sensing and marine biology research. In: Proc. IEEE OCEANS, pp. 1–4 (2006)

  7. Kahanov, Y., Royal, J.G.: Analysis of hull remains of the Dor D Vessel, Tantura Lagoon, Israel. Int. J. Nautical Archeol. 30, 257–265 (2001)

    Article  Google Scholar 

  8. Mobley, C.D.: Light and water: radiative transfer in natural waters. Academic Press, Cambridge (1994)

    Google Scholar 

  9. Ancuti, C., Ancuti, C.O., Haber, T., Bekaert, P.: Enhancing underwater images and videos by fusion. In: Proc. IEEE CVPR, pp. 81–88 (2012)

  10. Lu, H., et al.: Underwater image enhancement method using weighted guided trigonometric filtering and artificial light correction. J. Vis. Commun. Image Represent 38, 504–516 (2016)

    Article  Google Scholar 

  11. Drews Jr., P.L.J., Nascimento, E.R., Botelho, S.S.C., Campos, M.F.M.: Underwater depth estimation and image restoration based on single images. IEEE Comput. Graph. Appl. 36(2), 24–35 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Weijer, J.V.D., Gevers, T.: Color constancy based on the grey-edge hypothesis. In: IEEE Int. Conf. Image Process, pp. II-722-II-725 (2005)

  16. Finlayson, G.D., Trezzi, E.: Shades of gray and colour constancy. In: IS&T/SID Color Imag. Conf., pp. 37–41 (2004)

  17. Yang, K.F., Gao, S.B., Li, Y.J.: Efficient illuminant estimation for color constancy using gray pixels. IEEE Comput. Vis. Pattern Recognit. 2015, 2254–2263 (2015)

    Google Scholar 

  18. Nomura, K., Sugimura, D., Hamamoto, T.: Underwater image color correction using exposure-bracketing imaging. IEEE Signal Process. Lett. 25, 6 (2018)

    Article  Google Scholar 

  19. Ancuti, C.O., Ancuti, C., Vleeschouwer, C.D., Bekaert, P.: Color balance and fusion for underwater image enhancement. IEEE Trans. Image Process. 27, 1 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  20. Chang, H.H., Cheng, C.Y., Sung, C.C.: Single underwater image restoration based on depth estimation and transmission compensation. IEEE J. Ocean. Eng. 44, 4 (2019)

    Article  Google Scholar 

  21. Zhao, X., Jin, T., Qu, S.: Deriving inherent optical properties from background color and underwater image enhancement. Ocean Eng. 94, 163–172 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

  23. He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior (in English). IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)

    Article  Google Scholar 

  24. Peng, Y.T., Cao, K., Cosman, P.C.: Generalization of the dark channel prior for single image restoration. IEEE Trans. Image Process. 27, 6 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  25. Guo, J.K., Sung, C.C., Chang, H.H.: Improving visibility and fidelity of underwater images using an adaptive restoration algorithm. In: Proc. OCEANS Conf., Taipei, Taiwan, pp. 1–6 (2014)

  26. Borkar, S. , Bonde, S.V.: Underwater image restoration using single color channel prior. In: Proc. Int. Conf. Signal Inf. Process., pp. 1–4 (2016)

  27. Yu, H., Li, X., Lou, Q., Lei, C., Liu, Z.: Underwater image enhancement based on DCP and depth transmission map. Multimedia Tools Appl. 2020, 1 (2020)

    Google Scholar 

  28. Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Adv. Neural. Inf. Process. Syst.3, 2672–2680 (2014)

    Google Scholar 

  29. Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., Shi, W.: Photo-realistic single image super-resolution using a generative adversarial network. ArXiv e-prints (2016)

  30. Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al.: Photorealistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4681–4690 (2017)

  31. Yeh, R.A., Chen, C., Lim, T., Hasegawa-Johnson, M., Do, M.N.: Semantic image in-painting with perceptual and contextual losses. In: CoRR, abs/1607.07539 (2016)

  32. Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., Matas, J.: Deblurgan: blind motion deblurring using conditional adversarial networks. In: IEEE conference on computer vision and pattern recognition, pp. 8183–8192 (2018)

  33. Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., Matas, J.: DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better. In: IEEE conference on computer vision and pattern recognition (2019)

  34. Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: arxiv (2016)

  35. Li, J., Skinner, K.A., Eustice, R.M., Johnson-Roberson, M.: WaterGAN: unsupervised generative network to enable real-time color correction of monocular underwater images. IEEE Robot. Autom. Lett. 3(1), 387–394 (2018)

    Google Scholar 

  36. Fabbri, C., Islam, M.J., Sattar, J.: Enhancing underwater imagery using generative adversarial networks. In: ICRA pp. 7159–7165 (2018)

  37. Yu, X., Qu, Y., Hong, M.: Underwater-GAN: underwater image restoration via conditional generative adversarial network. In; ICPR, pp. 66–75 (2018)

  38. Wang, N., Zhou, Y., Han, F., Zhu, H., Zheng, Y.: UWGAN: underwater GAN for real-world underwater color restoration and dehazing. In: CVPR (2019)

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

    Article  Google Scholar 

  40. Land, E.H.: The Retinex theory of color vision. Sci. Am. 237(6), 108–128 (1977)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  42. Finlayson, G.D., Trezzi, E.: Shades of gray and colour constancy. In: Proc. 12th Color Imag. Conf., Color Sci., Syst. Appl., Soc. Imag. Sci. Technol., pp. 37–41 (2004)

  43. Gijsenij, A., Gevers, T.: Color constancy using natural image statistics and scene semantics. IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 687–698 (2011)

    Article  Google Scholar 

  44. Berman, D., Treibitz, T., Avidan, S.: Air-Light estimation using Haze-Lines. ICCP (2017)

  45. Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM Trans. Graph. 27(3), 73 (2008)

    Article  Google Scholar 

  46. Xu, L., Zheng, S., Jia, J.: Unnatural L0 sparse representation for natural image deblurring. IEEE Conf. Comput. Vis. Pattern Recog. 2013, 1107–1114 (2013)

    Google Scholar 

  47. Lou, Y., Bertozzi, A.L., Soatto, S.: Direct sparse deblurring. J. Math. Imag. Vis. 39(1), 1–12 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  48. Zhang, H., Yang, J., Zhang, Y., Huang, T.S.: Sparse representation based blind image deblurring. In: Proc. IEEE Int. Conf. Multimedia Expo, pp. 1–6 (2011)

  49. Cai, J.-F., Ji, H., Liu, C., Shen, Z.: Framelet based blind motion deblurring from a single image. IEEE Trans. Image Process. 21(2), 562–572 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  50. Cai, C.T., Meng, H.Y., Zhu, Q.D.: Blind Deconvolution for Image Deblurring Based on Edge Enhancement and Noise Suppression. IEEE Access 6, 58710–58718 (2018)

    Article  Google Scholar 

  51. Wang, Y., Yang, J., Yin, W., Zhang, Y.: A new alternating minimization algorithm for total variation image reconstruction. SIAM J. Imaging Sci. 1(3), 248–272 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  52. Cho, L.: Fast motion deblurring. ACM Trans. Graph. (TOG) 28, 45 (2009)

    Article  Google Scholar 

  53. Gao, S.B., Zhang, M., Zhao, Q., Zhang, X.S., Li, Y.J.: Underwater image enhancement using adaptive retinal mechanisms. IEEE Trans. Image Process. 28, 11 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  54. Berman, D., Levy, D., Avidan, S., Treibitz, T.: Underwater single image color restoration using haze-lines and a new quantitative dataset, TPAMI (2018)

  55. Panetta, K., Gao, C., Agaian, S.: Human-visual-system-inspired underwater image quality measures. IEEE J. Ocean. Eng. 41(3), 541–551 (2016)

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  57. Ancuti, C., Ancuti, C.O., De Vleeschouwer, C., Garcia, R., Bovik, A.C.: Multi-scale underwater descattering. In: Proc. 23rd Int. Conf. Pattern Recognit. (ICPR), pp. 4202–4207 (2016)

  58. Hasler, D., Susstrunk, S.E.: Measuring colorfulness in natural images. Proc. SPIE Int. Soc. Opt. Eng. 5007, 87–95 (2003)

    Google Scholar 

  59. Emberton, S., Chittka, L., Cavallaro, A.: Underwater image and video dehazing with pure haze region segmentation, CVIU (2017)

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (nos. 61673129, 51674109) and States Key Laboratory of Air Traffic Management System and Technology (no. SKLATM201907).

Author information

Authors and Affiliations

  1. College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, China

    Haiyang Meng, Chengtao Cai, Renjie Qiao & Feng Wang

  2. Key laboratory of Intelligent Technology and Application of Marine Equipment (Harbin Engineering University), Ministry of Education, Harbin, 150001, China

    Haiyang Meng, Chengtao Cai, Renjie Qiao & Feng Wang

  3. Heilongjiang Provincial Key Laboratory of Environment Intelligent Perception (Harbin Engineering University), Harbin, 150001, China

    Haiyang Meng, Chengtao Cai, Renjie Qiao & Feng Wang

  4. State Key Laboratory of Air Traffic Management System and Technology, Nanjing, China

    Yongjie Yan

Authors
  1. Haiyang Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongjie Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chengtao Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Renjie Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Feng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chengtao Cai.

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

Meng, H., Yan, Y., Cai, C. et al. A hybrid algorithm for underwater image restoration based on color correction and image sharpening. Multimedia Systems 28, 1975–1985 (2022). https://doi.org/10.1007/s00530-020-00693-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-020-00693-2

Keywords

Search

Navigation