KaydedilenlerDerginizi ekleyin

Dergiler / Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi / 2019 / Cilt: 11 - Sayı: 2

Adaptive Right Median Filter for Salt-and-Pepper Noise Removal

Uğur ERKAN
Levent GÖKREM
Serdar ENGİNOĞLU
Sayfa
542–550
Yayın Tarihi
2019-06-30
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Finally, we discuss the need for further research.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:In image processing, nonlinear filters are commonly used as a pre-process for noise removal before applying any advanced processing such as classification and clustering to an image.
📊
Temel Bulgular & Veri:Afterwards, we compare the results of ARMF with some of the known filters by using 12 test images and two image quality metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM).
💡
Sonuç & Katkı:Finally, we discuss the need for further research.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Image denoising · Noise removal · Nonlinear filters

Özet

In image processing, nonlinear filters are commonly used as a pre-process for noise removal before applying any advanced processing such as classification and clustering to an image. The adaptive filters being a kind of the nonlinear filters mainly perform better than the others in salt-and-pepper noise. In this paper, we first define a new median method, i.e. right median(rm). We then define a new adaptive nonlinear filter developed via rm, namely Adaptive Right Median Filter (ARMF), for saltand-pepper noise removal. Afterwards, we compare the results of ARMF with some of the known filters by using 12 test images and two image quality metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). The results show that ARMF outperforms the other methods at all the noise density except 80% and 90% in the mean percentages. Finally, we discuss the need for further research.

Abstract

In image processing, nonlinear filters are commonly used as a pre-process for noise removal before applying any advanced processing such as classification and clustering to an image. The adaptive filters being a kind of the nonlinear filters mainly perform better than the others in salt-and-pepper noise. In this paper, we first define a new median method, i.e. right median(rm). We then define a new adaptive nonlinear filter developed via rm, namely Adaptive Right Median Filter (ARMF), for saltand-pepper noise removal. Afterwards, we compare the results of ARMF with some of the known filters by using 12 test images and two image quality metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). The results show that ARMF outperforms the other methods at all the noise density except 80% and 90% in the mean percentages. Finally, we discuss the need for further research.

🔖

Bu Makaleye Atıf Yap

ERKAN, U., GÖKREM, L., ENGİNOĞLU, S. (2019). Adaptive Right Median Filter for Salt-and-Pepper Noise Removal. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, Cilt: 11 - Sayı: 2, 542–550. https://ajindex.com/articles/889384

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (12)

  1. Asuni, N., & Giachetti, A. (2014). TESTIMAGES: a Large-scale Archive for Testing Visual Devices and Basic Image Processing Algorithms. In A. Giachetti (Ed.), Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference. The Eurographics Association. https://doi.org/10.2312/stag.20141242
  2. Bai, T., Tan, J., Hu, M., & Wang, Y. (2014). A novel algorithm for removal of salt and pepper noise using continued fractions interpolation. Signal Processing. https://doi.org/10.1016/j.sigpro.2014.03.023
  3. Chan, R. H., Ho, C. W., & Nikolova, M. (2005). Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Transactions on Image Processing. https://doi.org/10.1109/TIP.2005.852196
  4. Erkan, U., & Gökrem, L. (n.d.). Tuz-Biber Gürültüsünde Tekrarsız Medyan Filtre Median Filter without Repetition in Salt and Peppers Noise. GBAD) Gaziosmanpasa Journal of Scientific Research Araştırma Makalesi, ISSN, 2146–8168.
  5. Erkan, U., & Gökrem, L. (2018). A new method based on pixel density in salt and pepper noise removal. Turkish Journal of Electrical Engineering and Computer Sciences. https://doi.org/10.3906/elk-1705-256
  6. Erkan, U., Gökrem, L., & Enginoğlu, S. (2018). Different applied median filter in salt and pepper noise. Computers and Electrical Engineering, 70, 789–798. https://doi.org/10.1016/j.compeleceng.2018.01.019
  7. Erkan, U., & Kilicman, A. (2016). Two new methods for removing salt-and-pepper noise from digital images. ScienceAsia. https://doi.org/10.2306/scienceasia1513-1874.2016.42.028
  8. Esakkirajan, S., Veerakumar, T., Subramanyam, A. N., & PremChand, C. H. (2011). Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median Filter. IEEE Signal Processing Letters. https://doi.org/10.1109/LSP.2011.2122333
  9. Gellert, A., & Brad, R. (2016). Context-based prediction filtering of impulse noise images. IET Image Processing, 10(6), 429–437. https://doi.org/10.1049/iet-ipr.2015.0702
  10. Han, J., Yue, J., Zhang, Y., & Bai, L. (2015). Local Sparse Structure Denoising for Low-Light-Level Image. IEEE Transactions on Image Processing. https://doi.org/10.1109/TIP.2015.2447735
  11. Hwang, H., & Haddad, R. A. (1995). Adaptive Median Filters: New Algorithms and Results. IEEE Transactions on Image Processing. https://doi.org/10.1109/83.370679
  12. Jiang, J., Zhang, L., & Yang, J. (2014). Mixed noise removal by weighted encoding with sparse nonlocal regularization. IEEE