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Sonar image denoising and segmentation techniques based on neutrosophic set

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Abstract

Due to the complex marine environment and the inherent characteristics of imaging equipment, sonar images often suffer from issues like speckle noise, weak edges, and shadows. To overcome these challenges, a noise reduction and segmentation technique for sonar images based on the neutral set domain is proposed. Firstly, the advantages of the Non-Subsampled Contourlet Transform are combined with the clustering capabilities of the neutrosophic set to process high-frequency sub-band noise. This approach effectively removes noise from sonar images while preserving edge information. Next, the neutrosophic set is utilized as a “transformation” function, and the concept of fuzziness is introduced to enhance the brightness and edge details of the true subset images. This enhancement improves the visibility of target objects in the sonar images. The self-adjusting spectral clustering technique is then applied to partition the true subsets into multiple independent subregions. By extracting only the brightness and narrowness features of these subregions, precise segmentation of target objects is achieved. The effectiveness and robustness of this proposed method are validated through the denoising and segmentation of sonar images containing various target objects. Importantly, it does not lead to missegmentation in shadowed or other interfering areas. In summary, this technique addresses the challenges of noise and segmentation in sonar images by combining NSCT, neutral sets, and spectral clustering, resulting in improved image quality and accurate target object segmentation.

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Availability of data and materials

Experimental data were obtained from the public dataset SCTD and the self-tested forward-looking sonar dataset DIDSON and aquatic plant energy data in the Erhai Sea, Yunnan Province. The data set is not available.

References

  1. Yang, D., Wang, C., Cheng, C., Pan, G., Zhang, F.: Semantic segmentation of side-scan sonar images with few samples. Electronics 11(19), 3002 (2022)

    Article  MATH  Google Scholar 

  2. Cao, Y., Liu, G.Y., Mu, L.L., et al.: Sonar image target detection based on multi-region optimal selection strategy. J. Northwestern Polytech. Univ. 41(01), 153–159 (2023)

    Article  MATH  Google Scholar 

  3. Yuan, F., Xiao, F., Zhang, K., et al.: Noise reduction for sonar images by statistical analysis and fields of experts. J. Vis. Commun. Image Represent. 74, 102995 (2021)

    Article  MATH  Google Scholar 

  4. Peng, C., Xuanwei, C., Dongdong, Z., et al.: Despeckling for side-scan sonar images based on adaptive block-matching and 3D filtering. Opto-Electron. Eng. 47(7), 190580-1–190580-10 (2020)

    Google Scholar 

  5. Wang, X., Yin, L., Gao, M., et al.: Denoising method for passive photon counting images based on block-matching 3D filter and non-subsampled contourlet transform. Sensors 19(11), 2462 (2019)

    Article  MATH  Google Scholar 

  6. Jia, D., Ge, Y.: Underwater image de-noising algorithm based on nonsubsampled contourlet transform and total variation. In: 2012 International Conference on Computer Science and Information Processing (CSIP). IEEE, pp. 76–80 (2012)

  7. Singh, P., Diwakar, M., Singh, S., et al.: A homomorphic non-subsampled contourlet transform based ultrasound image despeckling by novel thresholding function and self-organizing map. Biocybern. Biomed. Eng. 42(2), 512–528 (2022)

    Article  MATH  Google Scholar 

  8. Rahimizadeh, N., Hasanzadeh, R.P.R., Ghahramani, M., et al.: A neutrosophic based non-local means filter for despeckling of medical ultrasound images. In: 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE). IEEE, pp. 249–254 (2019)

  9. Jayaraman, M., Vellingiri, K., Guo, Y.: Neutrosophic set in medical image denoising. Neutrosophic Set in Medical Image Analysis, pp. 77–100. Academic Press (2019)

    Book  MATH  Google Scholar 

  10. Wang, X., Guo, L., Yin, J., et al.: Narrowband Chan-Vese model of sonar image segmentation: A adaptive ladder initialization approach. Appl. Acoust. 113, 238–254 (2016)

    Article  MATH  Google Scholar 

  11. Liu, G.Y., Cao, Y., Zeng, Z.Y., et al.: Underwater Multi-objects Segmentation Technology Based on Spectral Clustering with Multi-feature Weighting. J. Hunan Univ. Nat. Sci. 49(10), 51–60 (2022)

    MATH  Google Scholar 

  12. Huang, Y., Li, W., Yuan, F.: Speckle noise reduction in sonar image based on adaptive redundant dictionary. J. Mar. Sci. Eng. 8(10), 761 (2020)

    Article  MATH  Google Scholar 

  13. Mehta, N., Prasad, S.: Speckle noise reduction and entropy minimization approach for medical images. Int. J. Inf. Technol. 13, 1457–1462 (2021)

    MATH  Google Scholar 

  14. Lu, Y., Liu, R.W., Chen, F., et al.: Learning a deep convolutional network for speckle noise reduction in underwater sonar images. In: Proceedings of the 2019 11th International Conference on Machine Learning and Computing, pp. 445–450 (2019)

  15. Malini, S., Divya, V., Moni, R.S.: Improved methods of image denoising using non-sub sampled contourlet transform. Adv. Comput. Sci. Technol. 10(4), 549–560 (2017)

    MATH  Google Scholar 

  16. Bhargava, G.U., Gangadharan, S.V.: An effective method for image denoising using nonlocal means and statistics based guided filter in non-subsampled contourlet domain. Int. J. Intell. Eng. Syst. 12(3), 76–87 (2019)

    MATH  Google Scholar 

  17. Koundal, D., Sharma, B.: Advanced neutrosophic set-based ultrasound image analysis. Neutrosophic set in medical image analysis, pp. 51–73. Academic Press (2019)

    MATH  Google Scholar 

  18. Guo, Y., Sengür, A.: A novel image segmentation algorithm based on neutrosophic similarity clustering. Appl. Soft Comput. 25, 391–398 (2014)

    Article  MATH  Google Scholar 

  19. Jayaraman, M., Vellingiri, K., Guo, Y.: Neutrosophic set in medical image denoising. Neutrosophic Set in Medical Image Analysis, pp. 77–100 (2019)

  20. Sengür, A., Guo, Y.: Color texture image segmentation based on neutrosophic set and wavelet transformation. Comput. Vis. Image Underst. 115(8), 1134–1144 (2011)

    Article  MATH  Google Scholar 

  21. Jianhu, Z., Xiao, W., Hongmei, Z., et al.: Neutral ensemble and quantum particle swarm algorithm for side-scan sonar image segmentation. J. Surv. Mapp. 45(08), 935–942 (2016)

    MATH  Google Scholar 

  22. Song, S., Jia, Z., Yang, J., et al.: A fast image segmentation algorithm based on saliency map and neutrosophic set theory. IEEE Photon. J. 12(5), 1–16 (2020)

    Article  MATH  Google Scholar 

  23. Singh, P., Huang, Y.P., Lee, T.T.: A novel ambiguous set theory to represent uncertainty and its application to brain MR image segmentation. In: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, pp. 2460–2465 (2019)

  24. Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. Proc. Adv. Neural Inf. Process. Syst. 17, 1601–1608 (2005)

    Google Scholar 

  25. Liu, G.Y., Zeng, Z.Y., Cao, Y., et al.: Sonar image denoising based on density clustering and gray scale transformation in NSST domain. J. Hunan Univ. Nat. Sci. 49(08), 186–195 (2022)

    MATH  Google Scholar 

  26. Mohan, J., Krishnaveni, V., Guo, Y.: A neutrosophic approach of MRI denoising. In: 2011 International Conference on Image Information Processing. IEEE, pp. 1–6 (2011)

  27. Mohan, J., Thilaga Shri Chandra, A.P., Krishnaveni, V., et al.: Image denoising based on neutrosophic wiener filtering. In: Advances in Computing and Information Technology: Proceedings of the Second International Conference on Advances in Computing and Information Technology (ACITY) July 13–15, 2012, Chennai, India, vol. 2, pp. 861–869. Springer Berlin Heidelberg, (2013)

  28. Liu, G.Y., Bian, H.Y., Shen, Z.Y., Shi, H.: Research on spectral clustering denoising algorithm for morphological wavelet domain sonar images. Sens. Microsyst. 30(10), 41–43 (2011)

    MATH  Google Scholar 

  29. Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608–4622 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  30. Zhang, L., Zhang, L., Mou, X., et al.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  31. Sattar, F., Floreby, L., Salomonsson, G., et al.: Image enhancement based on a nonlinear multiscale method. IEEE Trans. Image Process. 6(6), 888–895 (1997)

    Article  MATH  Google Scholar 

  32. Elazab, A., Wang, C., Jia, F., et al.: Segmentation of brain tissues from magnetic resonance images using adaptively regularized kernel-based fuzzy C-means clustering. Comput. Math. Methods Med. 2015(5), 485495 (2015)

    MATH  Google Scholar 

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Acknowledgements

We thank Drs. Guangyu Liu and Enming Zhao for financial support as well as article visualization and linguistic logic; Wei Feng for experimental help; andWenxuan Liu and Chunli Yang for reviewing the figures and tables. All authors consented to publication and have no conflict of interest.

Funding

This work is supported by the Natural Science Foundation of China (No. 62065001), Open Fund Project of Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education [MIES-2023-02].

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L.B. and F.W. wrote the main manuscript text. L.G. and Z.E. are responsible for the overall structure planning of the paper, language inspection, financial support, etc. L.W. and Y.C. are responsible for code diagram writing and experimental conclusions.

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Correspondence to Guangyu Liu.

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Liu, B., Liu, G., Feng, W. et al. Sonar image denoising and segmentation techniques based on neutrosophic set. SIViP 19, 143 (2025). https://doi.org/10.1007/s11760-024-03625-z

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