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Dual-purpose method for de-hazing and enhancement of underwater and low-light images

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Abstract

There are many similarities between underwater images and low-light images, such as image blur and color distortion, but for such problems, there are few unified methods that can solve these problems well. This paper proposes a method based on multi-scale retinex color recovery (MSRCR) and color correction. First, color channel transfer (CCT) is used to preprocess the image. Then, a method of MSRCR and guided filtering is proposed to remove image fog. Finally, the statistical colorless slant correction fusion smoothing filter method is proposed to enhance the image, which improves the color contrast and sharpness of the image. Experiments have proved that the method proposed in this paper is effective in image de-hazing and enhancement.

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References

  1. Li, S., Kang, X.: Fast multi-exposure image fusion with median filter and recursive filter. IEEE Trans. Consum. Electron. 58(2), 626–632 (2012)

    Article  Google Scholar 

  2. Im, J., Jeon, J., Hayes, M.H., Paik, J.: Single image-based ghostfree high dynamic range imaging using local histogram stretching and spatially-adaptive denoising. IEEE Trans. Consum. Electron. 57(4), 1478–1484 (2011)

    Article  Google Scholar 

  3. Bertalmío, M., Levine, S.: Variational approach for the fusion of exposure bracketed pairs. IEEE Trans. Image Process. 22(2), 712–723 (2013)

    Article  MathSciNet  Google Scholar 

  4. Galdran, A., Pardo, D., Picon, A., Alvarez-Gila, A.: Automatic red-channel underwater image restoration. J. Vis. Commun. Image Represent. 26(2), 132–145 (2015)

    Article  Google Scholar 

  5. Ghani, A.S.A., Isa, N.A.M.: Underwater image quality enhancement through integrated color model with rayleigh distribution. Appl. Soft Comput. 27(3), 219–230 (2015)

    Article  Google Scholar 

  6. Li, C., Guo, J.: Underwater image enhancement by de-hazing and color correction. J. Electron. Imaging 24, 033023–033023 (2015)

    Article  Google Scholar 

  7. Li, C., Guo, J., Pang, Y., Chen, S., Wang, J.: Single underwater image restoration by blue-green channels dehazing and red channel correction. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 20–25 (2016)

  8. Liu, S., Zhang, Y.: Detail-preserving underexposed image enhancement via optimal weighted multi-exposure fusion. IEEE Trans. Consum. Electron. 65(3), 303–311 (2019)

    Article  Google Scholar 

  9. Li, Y., Ma, C., Zhang, T., Li, J., Ge, Z., Li, Y., Serikawa, S.: Underwater image high definition display using the multilayer perceptron and color feature-based SRCNN. IEEE Access. Environ. 7, 83721–83728 (2019)

    Article  Google Scholar 

  10. Pan, P.-W., Yuan, F., Cheng, E.: De-scattering and edge-enhancement algorithms for underwater image restoration. Front. Inf. Technol. Electron. Eng. 20(6), 862–871 (2019)

    Article  Google Scholar 

  11. Lu, H., Wang, D., Li, Y., Li, J., Li, X., Kim, H., Serikawa, S., Humar, I.: CONet: a cognitive ocean network. IEEE Wirel. Commun. 26(3), 90–96 (2019)

    Article  Google Scholar 

  12. Lu, H., Li, Y., Uemura, T., Kim, H., Serikawa, S.: Low illumination underwater light field images reconstruction using deep convolutional neural networks. Future Gener. Comput. Syst. 82, 142–148 (2018)

    Article  Google Scholar 

  13. Ancuti, C.O., Ancuti, C., De Vleeschouwer, C., Sbetr, M.: Color channel transfer for image dehazing. IEEE Signal Process. Lett. 26(9), 1413–1417 (2019)

    Article  Google Scholar 

  14. Lee, S., An, G.H., Kang, S.-J.: Deep chain HDRI: reconstructing a high dynamic range image from a single low dynamic range image. IEEE Access. 6, 49913–49924 (2018)

    Article  Google Scholar 

  15. Tanikawa, R., Fujisawa, T., Ikehara, M.: Image restoration based on weighted average of multiple blurred and noisy images. In: 2018 International Workshop on Advanced Image Technology (IWAIT), pp. 7–9 (2018)

  16. Vasu, S., Shenoi, A., Rajagopazan, A.N.: Joint HDR and super-resolution imaging in motion blur. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 7–10 (2018)

  17. Steffens, C., Drews, P.L.J., Botelho, S.S.: Deep learning based exposure correction for image exposure correction with application in computer vision for robotics. In: 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE), pp. 6–10 (2018)

  18. Dai, C., Lin, M., Wang, J., Hu, X.: Dual-purpose method for underwater and low-light image enhancement via image layer separation. IEEE Access 7, 178685–17869806 (2019)

    Article  Google Scholar 

  19. Jing, H., Yuanyuan, L.: Urban night image restoration algorithm based on space model. In: 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), pp. 27–29 (2018)

  20. Liu, Y., Yan, H., Gao, S., Yang, K.: Criteria to evaluate the fidelity of image enhancement by MSRCR. IET Image Proc. 12(6), 880–887 (2018)

    Article  Google Scholar 

  21. Han, Z., Lu, W., Yang, S., Liu, Q., Qi, J.: A new method of natural image defogging based on guided filtering optimization. Comput. Sci. Explor. 9(10), 1256–1262 (2015)

    Google Scholar 

  22. Fu, X., Zhuang, P., Huang, Y., Liao, Y., Zhang, X.-P., Ding, X.: A retinex-based enhancing approach for single underwater image. In: Proc. IEEE Int. Conf. Image Process., pp. 4572–4576 (2015)

  23. Yang, M., Sowmya, A., Wei, Z., Zheng, B.: Offshore Underwater image restoration using reflection-decomposition-based transmission map estimation. IEEE J. Ocean. Eng. 45(2), 521–533 (2020)

    Article  Google Scholar 

  24. Berman, D., Treibitz, T., Avidan, S.: Non-local image dehaz-ing. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 1674–1682 (2016)

  25. Li, Z., Zheng, J.: Single image de-hazing using globally guided image filtering. IEEE Trans. Image Process. 27(1), 442–450 (2018)

    Article  MathSciNet  Google Scholar 

  26. Min, D., Choi, S., Lu, J., Ham, B., Sohn, K., Do, M.: Fast global image smoothing based on weighted least squares. IEEE Trans. Image Process. 23(12), 5638–5653 (2014)

    Article  MathSciNet  Google Scholar 

  27. Liu, K., Liang, Y.Q.: Underwater image enhancement method based on adaptive attenuation-curve prior. Opt. Express 29(7), 10321–10345 (2021)

    Article  Google Scholar 

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Acknowledgements

Thanks to my teachers and classmates for their help in the paper writing; it was with their encouragement and guidance that I finally finished this paper. The authors acknowledge this paper was supported by the National Key Research and Development Program of China under Grant 2017YFC0804406. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. All the authors who participated in the writing of the manuscript and the review committee of our institution (Shandong University of Science and Technology) expressed their oral consent to the submission of the manuscript.

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Authors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Ke Liu

  2. Provincial Key Laboratory for Information Technology of Wisdom Mining of Shandong Province, Qingdao, China

    Yongquan Liang

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  1. Ke Liu
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  2. Yongquan Liang
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Correspondence to Ke Liu.

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Liu, K., Liang, Y. Dual-purpose method for de-hazing and enhancement of underwater and low-light images. Machine Vision and Applications 32, 107 (2021). https://doi.org/10.1007/s00138-021-01230-5

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  • DOI: https://doi.org/10.1007/s00138-021-01230-5

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