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FFBF: cluster-based Fuzzy Firefly Bayes Filter for noise identification and removal from grayscale images

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Abstract

Image denoising gains more attention in the field of image processing, which is essential to sustain the originality of the digital images in order to preserve all the essential information buried in the image. Even though lots of denoising techniques are available, the existing methods failed to denoise the image efficiently, and they are applicable only with lower noise probability. Thus, this paper proposes a Fuzzy Firefly Bayes Filter (FFBF) to perform the noise identification and removal. FFBF employs the Ck-based firefly Bayes algorithm and probabilistic clustering for identifying the presence of noisy pixel in the input image. The Ck-based Firefly Bayes algorithm is newly proposed by integrating the cuckoo search optimization, firefly optimization, and Bayes Classifier and it is based on the maximum posterior probability objective function. The proposed algorithm provides the best solution for the formulation of the binary matrix using the Bayes Classifier, which is subjected to fuzzy-based image denoising. The paper uses two standard images for experimentation, and the comparative analysis is performed in order to determine the superiority of the proposed method. The PSNR, SSIM, and SDME obtained for the proposed method are greater when compared with the existing methods, and the proposed method attained a maximum PSNR, SSIM, and SDME of 45.1696 dB, 0.8260, and 59.9684 dB.

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References

  1. Malik, M., Ahsan, F., Mohsin, S.: Adaptive image denoising using cuckoo algorithm. Soft Comput. 20(3), 925–938 (2016)

    Article  Google Scholar 

  2. Liu, D., Li, S., Sun, S., Ding, Z.: Application of fast particle swarm optimization algorithm in image denoising. Recent Adv. Comput. Sci. Inf. Eng. 126, 559–566 (2012)

    Article  Google Scholar 

  3. Lahmiri, S.: Denoising techniques in adaptive multi-resolution domains with applications to biomedical images. Healthc. Technol. Lett. 4(1), 25–29 (2016)

    Article  Google Scholar 

  4. Hao, R., Su, Z.: A patch-based low-rank tensor approximation model for multiframe image denoising. J. Computat. Appl. Math. 329, 125–133 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  5. Rafsanjani, H.K., Sedaaghi, M.H., Saryazdi, S.: An adaptive diffusion coefficient selection for image denoising. Digit. Signal Process 64, 71–82 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  6. Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Proceedings of the 5th international symposium, Sapporo, Japan, pp. 169-178, SAGA, October 26–28 (2009)

  7. Wang, Y., Yang, Y., Chen, T.: Spectral-spatial adaptive and well-balanced flow-based anisotropic diffusion for multispectral image denoising. J. Vis. Commun. Image Represent. 43, 185–197 (2017)

    Article  Google Scholar 

  8. Xu, S., Yang, X., Jiang, S.: A fast nonlocally centralized sparse representation algorithm for image denoising. Signal Process. 131, 99–112 (2017)

    Article  Google Scholar 

  9. Liu, X., Jing, X.-Y., Tang, G., Fei, W., Ge, Q.: Image denoising using weighted nuclear norm minimization with multiple strategies. Signal Process. 135, 39–252 (2017)

    Google Scholar 

  10. Pang, J.: Graph Laplacian regularization for image denoising: analysis in the continuous domain. IEEE Trans. Image Process. 26(4), 1770–1785 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  11. Roy, A., Singha, J., Devi, S.S.: Signal Process. Rabul Hussain Laskar, impulse noise removal using SVM classification based fuzzy filter from gray scale images 128, 262–273 (2016)

    Google Scholar 

  12. Jie Li; Qiangqiang Yuan; Huanfeng Shen; Liangpei Zhang: Noise Removal From Hyperspectral Image With Joint Spectral-Spatial Distributed Sparse Representation. IEEE Transactions on Geoscience and Remote Sensing 54(9), 5425–5439 (2016)

    Article  Google Scholar 

  13. Singh, K., Ranade, S.K., Singh, C.: Optik-Int. J. Light Electron Optics. Comparative performance analysis of various wavelet and nonlocal means based approaches for image denoising 131, 423–437 (2017)

    Google Scholar 

  14. Esakkirajan, S., Veerakumar, T., Subramanyam, A.N., PremChand, C.H.: Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter. IEEE Signal Process. Lett. 18(5), 287–290 (2011)

    Article  Google Scholar 

  15. Anila, S., Sivaraju, S.S., Devarajan, N.: A new contourlet based multiresolution approximation for MRI image noise removal. Natl. Acad. Sci. Lett. 40(1), 39–41 (2017)

    Article  Google Scholar 

  16. Ahmed, B.S., Rachid, H., Kamal, E.M., Sebti, F.: Multispectral image denoising with optimized vector non-local mean filter. Digit. Signal Process. 58, 115–126 (2016)

    Article  Google Scholar 

  17. de Paiva, J.L., Toledo, C.F.M., Pedrini, H.: An approach based on hybrid genetic algorithm applied to image denoising problem. Appl. Soft Comput. 46, 778–791 (2016)

    Article  Google Scholar 

  18. Subashini, P., Krishnaveni, M., Ane, B.K., Roller, D.: Wavelet based image denoising using ant colony optimization technique for identifying ice classes in SAR imagery. Soft Comput. Models Ind. Environ. Appl., 399–407 (2013)

  19. Kockanat, S., Karaboga, N.: Medical image denoising using metaheuristics. Stud. Computat. Intell. 704, 155–169 (2017)

    Google Scholar 

  20. Kannan, K., Perumal, S. Arumuga.: Combined denoising and fusion of multi focus images. Int. J. Adv. Res. Comput. Sci. Softw. Eng., 2(2) (2012)

  21. Ng, P.-E., Ma, K.-K.: A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Trans. Image Process. 15(6), 1506–1516 (2006)

    Article  Google Scholar 

  22. Varghese, J., Ghouse, M., Subash, S., Siddappa, M., Samiulla Khan, M., Hussain, O.B.: Efficient adaptive fuzzy-based switching weighted average filter for the restoration of impulse corrupted digital images. IET Image Process. 8(4), 199–206 (2014)

    Article  Google Scholar 

  23. Singh, K.M.: Vector median filter based on non-causal linear prediction for detection of impulse noise from images. Int. J. Comput. Sci. Eng. 7(4), 345–356 (2012)

    Google Scholar 

  24. Lin, T.C., Yu, P.T.: Adaptive two-pass median filter based on support vector machines for image restoration. Neural Comput. 16, 333–354 (2004)

    Article  MATH  Google Scholar 

  25. Arora, S., Singh, S.: Algorithm, the firefly optimization convergence analysis and parameter selection. Int. J. Comput. Appl. 69(3), 975–8887 (2013)

    Google Scholar 

  26. Wang, H., Wang, W., Zhou, X., Sun, H., Zhao, J.: Firefly algorithm with neighborhood attraction. Inf. Sci. 382—-383, 374–387 (2017)

    Article  Google Scholar 

  27. Venkata Vijaya Geeta, P., Ravi Kiran Varma, P.: Cuckoo search optimization and its applications: a review. Int. J. Adv. Res. Comput. Commun. Eng. 5(11), 556–561 (2016)

    Google Scholar 

  28. Zhang, H., Jiang, L., Su, J.: The optimality of naive Bayes. In: Proceedings of the Seventeenth Florida Artificial Intelligence Research Society Conference, In FLAIRS Conference (2004)

Download references

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Authors and Affiliations

  1. Department of CSE, JNTUH, Hyderabad, India

    S. Vijaya Kumar

  2. YSR Engineering College of Yogivemana University, Proddatur, Andhra Pradesh, 516360, India

    C. Nagaraju

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  1. S. Vijaya Kumar
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  2. C. Nagaraju
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Correspondence to S. Vijaya Kumar.

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Kumar, S.V., Nagaraju, C. FFBF: cluster-based Fuzzy Firefly Bayes Filter for noise identification and removal from grayscale images. Cluster Comput 22 (Suppl 1), 1289–1311 (2019). https://doi.org/10.1007/s10586-017-1601-1

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  • DOI: https://doi.org/10.1007/s10586-017-1601-1

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