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An Underwater Image Color Correction Algorithm Based on Underwater Scene Prior and Residual Network

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Artificial Intelligence and Security (ICAIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13339))

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Abstract

Color distortion in underwater scenes affect the accuracy of pattern recognition, visual understanding and key feature extraction work of underwater robots. In this paper, we propose a method to correct color distortion in underwater images by exploiting prior knowledge of the underwater scene and combining it with a residual network. A dataset with the color deviation only is created by the proposed method according to the underwater image imaging model. The residual network is adopted to train the dataset with only color shift so that they have the ability to improve color deviation. Training the network only in the presence of color-biased dataset provides the network to be more specialized, extract single features even better and the number of training samples is also reduced. The experimental results demonstrate that the method in this paper can fully preserve the local details of the image with the color correction, leading to good qualitative and quantitative results in comparison with other image restoration and enhancement methods.

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References

  1. Kong, X., Zhao, H., Qiao, Y., Dong, C.: ClassSR: a general framework to accelerate superresolution networks by data characteristic. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2021), pp. 12016–12025 (2021)

    Google Scholar 

  2. Liu, Y., Xie, Y., Yang, J., Zuo, X., Zhou, B.: Target classification and recognition for high resolution remote sensing images: using the parallel cross-modal neural cognitive computing algorithm. IEEE Geosci. Remote Sens. Mag. 8(3), 50–62 (2020)

    Article  Google Scholar 

  3. Zhang, X., Zhang, W., Sun, X., Sun, S.: Jha: A robust 3-d medical watermarking based on wavelet transform for data protection. Comput. Syst. Sci. Eng. 41(3), 1043–1056 (2022)

    Article  Google Scholar 

  4. Zhang, X., Sun, X., Sun, W., Xu, T., Wang, P., Jha, S.K.: Deformation expression of soft tissue based on bp neural network. Intell. Autom. Soft Comput. 32(2), 1041–1053 (2022)

    Article  Google Scholar 

  5. Zhang, X., Sun, X., Sun, X., Sun, W., Jha, S.K.: Robust reversible audio watermarking scheme for telemedicine and privacy protection. Comput. Mater. Continua 71(1), 3035–3050 (2022)

    Article  Google Scholar 

  6. Iqbal, K., Odetayo, M.O., James, A.E., Salam, R.A., Talib, A.Z.: Enhancing the low quality images using unsupervised colour correction method. In: 2010 IEEE International Conference on Systems, Man and Cybernetics, pp. 1703–1709 (2010)

    Google Scholar 

  7. Ghani, A., Isa, N.: Underwater image quality enhancement through integrated color model with Rayleigh distribution. Appl. Soft Comput. 27, 219–230 (2014)

    Article  Google Scholar 

  8. Ancuti, C.O., Ancuti, C., Vleeschouwer, C.D., Bekaert, P.: Color balance and fusion for underwater image enhancement. IEEE Trans. Image Process. 27(99), 379–393 (2017)

    MathSciNet  MATH  Google Scholar 

  9. Jin, K., Wang, S.: Image denoising based on the asymmetric Gaussian mixture model. J. Internet Things 2(1), 1–11 (2020)

    Article  Google Scholar 

  10. He, K., Jian, S., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)

    Article  Google Scholar 

  11. Chiang, J.Y., Chen, Y.C.: Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans. Image Process. 21(4), 1756–1769 (2012)

    Article  MathSciNet  Google Scholar 

  12. Peng, Y.T., Cao, K., Cosman, P.: Generalization of the dark channel prior for single image restoration. IEEE Trans. Image Process. 27, 2856–2868 (2018)

    Article  MathSciNet  Google Scholar 

  13. Guo, Y., Li, H., Zhuang, P.: Underwater image enhancement using a multiscale dense generative adversarial network. IEEE J. Oceanic Eng. 45(3), 862–870 (2020)

    Article  Google Scholar 

  14. Alenezi, F.: Image dehazing based on pixel guided cnn with pam via graph cut. Comput. Mater. Continua 71(2), 3425–3443 (2022)

    Article  Google Scholar 

  15. Li, C., et al.: An underwater image enhancement benchmark dataset and beyond. IEEE Trans. Image Process. 29, 4376–4389 (2020)

    Article  Google Scholar 

  16. Tan, R. T.: Visibility in bad weather from a single image. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), Anchorage, Alaska, USA, pp. 1–8 (2008)

    Google Scholar 

  17. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR2016), pp. 770–778 (2016)

    Google Scholar 

  18. Menaker, D., Treibitz, T., Avidan, S.: Color restoration of underwater images. In: British Machine Vision Conference (2017)

    Google Scholar 

  19. Li, C., Anwar, S.: Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recogn. 98(1), 107038 (2019)

    Google Scholar 

  20. Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from rgbd images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33715-4_54

    Chapter  Google Scholar 

  21. Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics Gems, pp. 474–485 (1994)

    Google Scholar 

  22. Kashif, I., Salam, R.A., Azam, O., Talib, A.Z.: Underwater image enhancement using an integrated colour model. IAENG Int. J. Comput. Sci. 34(2), 239–244 (2007)

    Google Scholar 

  23. Zhou, W., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4), 600–612 (2004)

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Yang, M., Sowmya, A.: An underwater color image quality evaluation metric. IEEE Trans. Image Process. 24(12), 6062–6071 (2015). https://doi.org/10.1109/TIP.2015.2491020

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgement

This work was supported by National Key Research and Development Program of China (Grant: 2018YFB1404400), Hainan Provincial Natural Science Foundation of China (Grant: 2019CXTD400), The Scientific Research Fund Project of Hainan University (Grant: KYQD(ZR)-21007, KYQD(ZR)-21008), Hainan Provincial Natural Science Foundation of China (621MS019), The Scientific Research Fund Project for Youth Teachers of Hainan University (Grant: HDQN202103).

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Authors and Affiliations

  1. School of Information and Communication Engineering, Hainan University, Haikou, 570228, China

    Mengxing Huang, Jinjin Ye, Shenghan Zhu, Yang Chen, Yuanyuan Wu, Di Wu, Siling Feng & Feng Shu

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  1. Mengxing Huang
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  2. Jinjin Ye
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  3. Shenghan Zhu
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  4. Yang Chen
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  5. Yuanyuan Wu
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  6. Di Wu
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  7. Siling Feng
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  8. Feng Shu
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Corresponding author

Correspondence to Yuanyuan Wu .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Huang, M. et al. (2022). An Underwater Image Color Correction Algorithm Based on Underwater Scene Prior and Residual Network. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13339. Springer, Cham. https://doi.org/10.1007/978-3-031-06788-4_11

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  • DOI: https://doi.org/10.1007/978-3-031-06788-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06787-7

  • Online ISBN: 978-3-031-06788-4

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