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An Adaptive Weighted Min-Mid-Max Value Based Filter for Eliminating High Density Impulsive Noise

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Abstract

This paper presents a novel algorithm to filter impulsive noise at very high noise density (\(\ge 85\%\)). The proposed algorithm initially makes an accurate decision and selects a window which has sufficient information for denoising. Within the selected window, the proposed algorithm computes maximum, minimum, middle values along with their weights to restore noisy pixel. The performance of proposed filter is evaluated on natural and medical images with varying noise density. The proposed filter showed tremendous performance at high noise densities in terms of quantitative metrics and visual representation. Even at noise densities as high as 97% and 99%, the proposed filter is able to retrieve the details of the image. The proposed filter on an average improves the peak signal to noise ratio value by 10% in medical images over the existing.

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References

  1. Astola, J., & Kuosmaneen, P. (1997). Fundamentals of nonlinear digital filtering. Boca Raton, FL: CRC.

    Google Scholar 

  2. Pitas, I., & Venetsanopoulos, A. N. (2013). Nonlinear digital filters: Principles and applications (Vol. 84). Springer.

  3. Zhang, S., & Karim, M. A. (2002). A new impulse detector for switching median filters. IEEE Signal Processing Letters, 9(11), 360–363.

    Article  Google Scholar 

  4. Ng, P.-E., & Ma, K.-K. (2006). A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Transactions on Image Processing, 15(6), 1506–1516.

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Li, Z., Liu, G., Xu, Y., & Cheng, Y. (2014). Modified directional weighted filter for removal of salt and pepper noise. Pattern Recognition Letters, 40, 113–120.

    Article  Google Scholar 

  7. Arora, S., Hanmandlu, M., & Gupta, G. (2018). Filtering impulse noise in medical images using information sets. Pattern Recognition Letters.

  8. Ramachandran, V., & Kishorebabu, V. (2019). A tri-state filter for the removal of salt and pepper noise in mammogram images. Journal of Medical Systems, 43(2), 40.

    Article  Google Scholar 

  9. Murugan, K., Arunachalam, V., & Karthik, S. (2019). Hybrid filtering approach for retrieval of MRI image. Journal of Medical Systems, 43(1), 9.

    Article  Google Scholar 

  10. Hwang, H., & Haddad, R. A. (1995). Adaptive median filters: New algorithms and results. IEEE Transactions on Image Processing, 4(4), 499–502.

    Article  Google Scholar 

  11. Bhadouria, V. S., Ghoshal, D., & Siddiqi, A. H. (2014). A new approach for high density saturated impulse noise removal using decision-based coupled window median filter. Signal, Image and Video Processing, 8(1), 71–84.

    Article  Google Scholar 

  12. Vijaykumar, V., Mari, G. S., & Ebenezer, D. (2014). Fast switching based median-mean filter for high density salt and pepper noise removal. AEU-International Journal of Electronics and Communications, 68(12), 1145–1155.

    Article  Google Scholar 

  13. Aiswarya, K., Jayaraj, V., & Ebenezer, D. (2010). A new and efficient algorithm for the removal of high density salt and pepper noise in images and videos. In 2010 second international conference on computer modeling and simulation (Vol. 4). IEEE, pp. 409–413.

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

    Article  Google Scholar 

  15. Faragallah, O. S., & Ibrahem, H. M. (2016). Adaptive switching weighted median filter framework for suppressing salt-and-pepper noise. AEU-International Journal of Electronics and Communications, 70(8), 1034–1040.

    Article  Google Scholar 

  16. Thanh, D. N. H., Hien, N. N., & Prasath, S. (2020). Adaptive total variation L1 regularization for salt and pepper image denoising. Optik, 208, 163677.

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Sohi, P. J. S., Sharma, N., Garg, B., & Arya, K. Noise density range sensitive mean-median filter for impulse noise removal. In Innovations in computational intelligence and computer vision (pp. 150–162). Springer.

  19. Garg, B., & Arya, K. V. (2020). Four stage median-average filter for healing high density salt and pepper noise corrupted images. Multimedia Tools and Applications, 79(43), 32305–32329.

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Satti, P., Sharma, N., & Garg, B. (2020). Min–Max average pooling based filter for impulse noise removal. IEEE Signal Processing Letters, 27, 1475–1479.

    Article  Google Scholar 

  22. Garg, B. (2020). An adaptive minimum–maximum value-based weighted median filter for removing high density salt and pepper noise in medical images. International Journal of Ad Hoc and Ubiquitous Computing, 35(2), 84–95.

    Article  Google Scholar 

  23. Lu, C.-T., Chen, Y.-Y., Wang, L.-L., & Chang, C.-F. (2016). Removal of salt-and-pepper noise in corrupted image using three-values-weighted approach with variable-size window. Pattern Recognition Letters, 80, 188–199.

    Article  Google Scholar 

  24. Erkan, U.(2018).Different applied median filter in salt and pepper noise. Computers and Electrical Engineering, 70, 789–798.

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Authors and Affiliations

  1. Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Nikhil Sharma, Prateek Jeet Singh Sohi & Bharat Garg

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  1. Nikhil Sharma
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  2. Prateek Jeet Singh Sohi
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  3. Bharat Garg
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Correspondence to Bharat Garg.

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Sharma, N., Sohi, P.J.S. & Garg, B. An Adaptive Weighted Min-Mid-Max Value Based Filter for Eliminating High Density Impulsive Noise. Wireless Pers Commun 119, 1975–1992 (2021). https://doi.org/10.1007/s11277-021-08314-5

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