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Different Adaptive Modified Riesz Mean Filter For High-Density Salt-and-Pepper Noise Removal in Grayscale Images

Year 2021, Issue: 23, 359 - 367, 30.04.2021
https://doi.org/10.31590/ejosat.873312

Abstract

This paper proposes a new Different Adaptive Modified Riesz Mean Filter (DAMRmF), for high-density salt-and-pepper noise (SPN) removal. DAMRmF operationalizes a pixel weight function and adaptivity condition of Adaptive Median Filter (AMF). In the simulation, the proposed filter is compared with Adaptive Frequency Median Filter (AFMF), Three-Values-Weighted Method (TVWM), Unbiased Weighted Mean Filter (UWMF), Different Applied Median Filter (DAMF), Adaptive Weighted Mean Filter (AWMF), Adaptive Cesáro Mean Filter (ACmF), Adaptive Riesz Mean Filter (ARmF), and Improved Adaptive Weighted Mean Filter (IAWMF) for 20 traditional test images with noise levels from 60% to 90%. The results show that DAMRmF outperforms the state-of-the-art filters in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) values. Moreover, DAMRmF also performs better than the state-of-the-art filters concerning mean PSNR and SSIM results. We finally discuss DAMRmF for further research

References

  • Enginoğlu, S., Erkan, U., & Memiş, S., (2019). Pixel similarity-based adaptive Riesz mean filter for salt-and-pepper noise removal, Multimedia Tools and Applications, 78(24), 35401–35418.
  • Enginoğlu, S., Erkan, U., & Memiş, S., (2020). Adaptive Cesáro mean filter for salt-and-pepper noise removal, El-Cezeri Journal of Science and Engineering, 7(1), 304–314. Erkan, U., Enginoğlu, S., Thanh, D. N. H., & Hieu, L. M., (2020a). Adaptive frequency median filter for the salt-and-pepper denoising problem, IET Image Processing, 14(7), 1291–1302.
  • Erkan, U., Gökrem, L., & Enginoğlu, S., (2018). Different applied median filter in salt and pepper noise, Computer and Electrical Engineering, 70, 789–798.
  • Erkan, U., Thanh, D. N. H., Enginoğlu, S., & Memiş, S., (2020b). Improved adaptive weighted mean filter for salt-and-pepper noise removal, 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, Turkey, pp. 1–5.
  • Gonzalez, R. C., & Woods, R. E., (2018). Digital image processing. New York: Pearson.
  • Hausen, R., & Robertson, B. E., (2020). Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. The Astrophysical Journal Supplement Series, 248(20), 1–37.
  • Hwang, H., & Haddad, R. A., (1995) Adaptive Median Filters: New Algorithms and Results. IEEE Transactions on Image Processing, 4(4), 499–502.
  • Kandemir, C., Kalyoncu, C., & Toygar, Ö., (2015). A weighted mean filter with spatial-bias elimination for impulse noise removal, Digital Signal Processing, 46, 164–174.
  • 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.
  • Öziç, M. Ü., & Özşen, S., (2020). Comparison global brain volume ratios on Alzheimer’s disease using 3D T1 weighted MR images. European Journal of Science and Technology, (18), 599–606.
  • Pratt, W. K. (1975) Semiannual Technical Report. Image Processing Institute, University of Southern California.
  • Tukey, J. W. (1977) Exploratory Data Analysis, Reading, MA: Addison­Wesley.
  • Wang, Z., Bovik, A. C., Sheikh, H. R., Simoncelli, E. P., (2004) Image quality assessment: From error visibility to structural similarity, IEEE Transactions on Image Processing, 13(4), 600–612.
  • Weber, A. G., (1997) The USC-SIPI image database version 5. University of Southern California, Viterbi School of Engineering, Signal and Image Processing Institute, Los Angeles, CA, USA: USC SIPI Techical Report 315, pp. 1–24.
  • Zeren, M. T., Aytulun, S. K. & Kırelli, Y., (2020). Comparison of SSD and faster R-CNN algorithms to detect the airports with data set which obtained from unmanned aerial vehicles and satellite images. European Journal of Science and Technology, (19), 643–658.
  • Zhang, P., & Li, F., (2014). A new adaptive weighted mean filter for removing salt-and-pepper noise, IEEE Signal Processing Letters, 21(10), 1280–1283.

Gri Tonlamalı Görüntülerdeki Yüksek Yoğunluklu Tuz ve Biber Gürültüsünü Kaldırmak için Farklı Uyarlamalı Modifiye Riesz Ortalama Filtresi

Year 2021, Issue: 23, 359 - 367, 30.04.2021
https://doi.org/10.31590/ejosat.873312

Abstract

Bu makale, yüksek yoğunluklu tuz ve biber gürültüsünün (SPN) giderilmesi için yeni bir Farklı Uyarlamalı Modifiye Riesz Ortalama Filtresi (DAMRmF) önermektedir. DAMRmF, bir piksel ağırlık fonksiyonu ve Uyarlamalı Medyan Filtresinin (AMF) uyarlanabilirlik koşulunu çalıştırır. Deneysel çalışmada önerilen filtre, %60 ve %90 kadar çeşitli gürültü yoğunluklarındaki 20 geleneksel test görüntüsü için Uyarlanabilir Frekans Medyan Filtresi (AFMF), Üç Değerli Ağırlıklı Yöntem (TVWM), Tarafsız Ağırlıklı Ortalama Filtresi (UWMF), Farklı Uygulanan Medyan Filtresi (DAMF), Uyarlamalı Ağırlıklı Ortalama Filtresi (AWMF), Uyarlamalı Cesáro Ortalama Filtresi (ACmF), Uyarlamalı Riesz Ortalama Filtresi (ARmF) ve Geliştirilmiş Uyarlamalı Ağırlıklı Ortalama Filtresi (IAWMF) karşılaştırılır. Sonuçlar, DAMRmF'nin Tepe Sinyal-Gürültü Oranı (PSNR) ve Yapısal Benzerlik (SSIM) değerleri açısından son teknoloji filtrelerden daha iyi performans sergilediğini göstermektedir. Ayrıca, ortalama PSNR ve SSIM sonuçlarına göre de DAMRmF son teknoloji filtrelerden daha iyi bir performansa sahiptir. Son olarak, gelecek çalışmalar için DAMRmF'yi tartışıyoruz.

References

  • Enginoğlu, S., Erkan, U., & Memiş, S., (2019). Pixel similarity-based adaptive Riesz mean filter for salt-and-pepper noise removal, Multimedia Tools and Applications, 78(24), 35401–35418.
  • Enginoğlu, S., Erkan, U., & Memiş, S., (2020). Adaptive Cesáro mean filter for salt-and-pepper noise removal, El-Cezeri Journal of Science and Engineering, 7(1), 304–314. Erkan, U., Enginoğlu, S., Thanh, D. N. H., & Hieu, L. M., (2020a). Adaptive frequency median filter for the salt-and-pepper denoising problem, IET Image Processing, 14(7), 1291–1302.
  • Erkan, U., Gökrem, L., & Enginoğlu, S., (2018). Different applied median filter in salt and pepper noise, Computer and Electrical Engineering, 70, 789–798.
  • Erkan, U., Thanh, D. N. H., Enginoğlu, S., & Memiş, S., (2020b). Improved adaptive weighted mean filter for salt-and-pepper noise removal, 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, Turkey, pp. 1–5.
  • Gonzalez, R. C., & Woods, R. E., (2018). Digital image processing. New York: Pearson.
  • Hausen, R., & Robertson, B. E., (2020). Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. The Astrophysical Journal Supplement Series, 248(20), 1–37.
  • Hwang, H., & Haddad, R. A., (1995) Adaptive Median Filters: New Algorithms and Results. IEEE Transactions on Image Processing, 4(4), 499–502.
  • Kandemir, C., Kalyoncu, C., & Toygar, Ö., (2015). A weighted mean filter with spatial-bias elimination for impulse noise removal, Digital Signal Processing, 46, 164–174.
  • 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.
  • Öziç, M. Ü., & Özşen, S., (2020). Comparison global brain volume ratios on Alzheimer’s disease using 3D T1 weighted MR images. European Journal of Science and Technology, (18), 599–606.
  • Pratt, W. K. (1975) Semiannual Technical Report. Image Processing Institute, University of Southern California.
  • Tukey, J. W. (1977) Exploratory Data Analysis, Reading, MA: Addison­Wesley.
  • Wang, Z., Bovik, A. C., Sheikh, H. R., Simoncelli, E. P., (2004) Image quality assessment: From error visibility to structural similarity, IEEE Transactions on Image Processing, 13(4), 600–612.
  • Weber, A. G., (1997) The USC-SIPI image database version 5. University of Southern California, Viterbi School of Engineering, Signal and Image Processing Institute, Los Angeles, CA, USA: USC SIPI Techical Report 315, pp. 1–24.
  • Zeren, M. T., Aytulun, S. K. & Kırelli, Y., (2020). Comparison of SSD and faster R-CNN algorithms to detect the airports with data set which obtained from unmanned aerial vehicles and satellite images. European Journal of Science and Technology, (19), 643–658.
  • Zhang, P., & Li, F., (2014). A new adaptive weighted mean filter for removing salt-and-pepper noise, IEEE Signal Processing Letters, 21(10), 1280–1283.
There are 16 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Samet Memiş 0000-0002-0958-5872

Uğur Erkan 0000-0002-2481-0230

Publication Date April 30, 2021
Published in Issue Year 2021 Issue: 23

Cite

APA Memiş, S., & Erkan, U. (2021). Different Adaptive Modified Riesz Mean Filter For High-Density Salt-and-Pepper Noise Removal in Grayscale Images. Avrupa Bilim Ve Teknoloji Dergisi(23), 359-367. https://doi.org/10.31590/ejosat.873312