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DIBS: distance- and intensity-based separation filter for high-density impulse noise removal

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Abstract

Noise is an unwanted element that degrades the quality of digital images. Salt and pepper noise is a type of noise that is introduced in one or more steps during image acquisition, enrolment, or transmission. It is therefore important to apply superior restoration methods to mitigate the noise. In this paper, a novel distance- and intensity-based separation filter is proposed wherein the denoised image is obtained by processing subsets of noise-free pixels. The core concept revolves around discarding less relevant information to get a smaller set of relevant pixel values. This filter removes color streaks and distortions that often appear in other filters at high salt and pepper noise. The quantitative comparisons on various standard images reveal that the proposed method outperforms state-of-the-art noise removal filters in terms of overall image detail restoration; achieving better noise removal, especially at higher noise levels. A high-level hardware architecture is also provided.

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References

  1. Wang, Z., Zhang, D.: Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Trans. Circ. Syst. II Analog Dig. Signal Process. 46(1), 78–80 (1999)

    Google Scholar 

  2. Faragallah, O.S., Ibrahem, H.M.: Adaptive switching weighted median filter framework for suppressing salt-and-pepper noise. AEU Int. J. Electron. Commun. 70(8), 1034–1040 (2016)

    Article  Google Scholar 

  3. Patel, P., Majhi, B., Jena, B., Tripathy, C.: Dynamic adaptive median filter (damf) for removal of high density impulse noise. Int. J. Image Graph. Signal Process. 4(11), 53 (2012)

    Article  Google Scholar 

  4. Veerakumar, T., Esakkirajan, S., Vennila, I.: Recursive cubic spline interpolation filter approach for the removal of high density salt-and-pepper noise. Signal Image Video Process. 8(1), 159–168 (2014)

    Article  Google Scholar 

  5. Vijaykumar, V., Mari, G.S., Ebenezer, D.: Fast switching based median-mean filter for high density salt and pepper noise removal. AEU Int. J. Electron. Commun. 68(12), 1145–1155 (2014)

    Article  Google Scholar 

  6. Balasubramanian, G., Chilambuchelvan, A., Vijayan, S., Gowrison, G.: An extremely fast adaptive high-performance filter to remove salt and pepper noise using overlapping medians in images. Imaging Sci. J. 64(5), 241–252 (2016)

    Article  Google Scholar 

  7. Appiah, O., Asante, M., Hayfron-Acquah, J.B.: Improved approximated median filter algorithm for real-time computer vision applications. J. King Saud Univ. Comput. Inf. Sci. 34(3), 782–792 (2020)

    Google Scholar 

  8. 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 

  9. Nair, M.S., Revathy, K., Tatavarti, R.: Congress on image and signal processing, vol 1. IEEE 2008, 426–431 (2008)

    Google Scholar 

  10. Vijaykumar, V., Vanathi, P., Kanagasabapathy, P., Ebenezer, D.: High density impulse noise removal using robust estimation based filter. IAENG Int. J. Comput. Sci. 35, 3 (2008)

    Google Scholar 

  11. Esakkirajan, S., Veerakumar, T., Subramanyam, A.N., PremChand, C.: 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)

  12. Eng, H.-L., Ma, K.-K.: Noise adaptive soft-switching median filter. IEEE Trans. Image Process. 10(2), 242–251 (2001)

  13. Erkan, U., GöKREM, L.: A new method based on pixel density in salt and pepper noise removal. Turk. J. Electr. Eng. Comput. Sci. 26(1), 162–171 (2018)

  14. Satti, P., Sharma, N., Garg, B.: Min-max average pooling based filter for impulse noise removal. IEEE Signal Process. Lett. 27, 1475–1479 (2020)

    Article  Google Scholar 

  15. Erkan, U., Enginoğlu, S., Thanh, D.N., et al.: Adaptive frequency median filter for the salt and pepper denoising problem. IET Image Process. 14(7), 1291–1302 (2019)

    Article  Google Scholar 

  16. Thanh, D.N., Prasath, V.S., Phung, T.K., Hung, N.Q.: Impulse denoising based on noise accumulation and harmonic analysis techniques. Optik 241, 166163 (2020)

    Article  Google Scholar 

  17. P. J. S. Sohi, N. Sharma, B. Garg, and K. Arya (eds) Noise density range sensitive mean-median filter for impulse noise removal In: Innovations in Computational Intelligence and Computer Vision. Springer, pp. 150–162 (2020)

  18. Garg, B., Arya, K.: Four stage median-average filter for healing high density salt and pepper noise corrupted images. Multimed. Tools Appl. 79(43), 305–329 (2020)

  19. Garg, B.: Restoration of highly salt-and-pepper-noise-corrupted images using novel adaptive trimmed median filter. Signal Image Video Process. 14(8), 1555–1563 (2020)

    Article  Google Scholar 

  20. Veerakumar, T., Subudhi, B.N., Esakkirajan, S.: Empirical mode decomposition and adaptive bilateral filter approach for impulse noise removal. Exp. Syst. Appl. 121, 18–27 (2019)

    Article  Google Scholar 

  21. Chen, J., Li, F.: Denoising convolutional neural network with mask for salt and pepper noise. IET Image Process. 13(13), 2604–2613 (2019)

    Article  Google Scholar 

  22. Thanh, D.N.H., Hien, N.N., Prasath, S., et al.: Adaptive total variation l1 regularization for salt and pepper image denoising. Optik 208, 163677 (2020)

    Article  Google Scholar 

  23. Kumar, S.V., Nagaraju, C.: Support vector neural network based fuzzy hybrid filter for impulse noise identification and removal from gray-scale image. J. King. Saud. Uni. Comput. Inf. Sci. 33(7), 820–835 (2018)

    Google Scholar 

  24. Satti, P., Shrotriya, V., Garg, B., Thanh, D.N.: Intensity bound limit filter for high density impulse noise removal. J. Ambient Intell. Hum. Comput. 5, 1–23 (2022)

    Google Scholar 

  25. Mukhopadhyay, R., Gupta, P., Satti, P., Garg, B.: Adaptive radii selection based inpainting method for impulse noise removal. Multimed. Tools Appl. 5, 1–20 (2023)

    Google Scholar 

  26. Wang, Z., 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 

  27. Usc-sipi image database website. [Online]. Available: https://sipi.usc.edu/database/database.php?volume=misc &image=13#top

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Funding

This work is supported by Thapar Institute of Engineering Technology (TIET), Patiala, India.

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Authors and Affiliations

  1. Electronics and Communication Engineering Department, Thapar Institute of Engineering and Technology Patiala, 147001, Patiala, India

    Piyush Satti, Vaibhav Shrotriya & Bharat Garg

  2. Department of Pediatrics, University of Cincinnati, Oh, USA

    V. B. Surya Prasath

Authors
  1. Piyush Satti
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  2. Vaibhav Shrotriya
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  3. Bharat Garg
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  4. V. B. Surya Prasath
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Contributions

The authors declare equal contribution by each authors. Vaibhav and Piyush did the algorithm implementation and well as the simulation while the other two authors were involved in paper writing and concept generation.

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Correspondence to Bharat Garg.

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Satti, P., Shrotriya, V., Garg, B. et al. DIBS: distance- and intensity-based separation filter for high-density impulse noise removal. SIViP 17, 4181–4188 (2023). https://doi.org/10.1007/s11760-023-02650-8

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  • DOI: https://doi.org/10.1007/s11760-023-02650-8

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