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Tuz-Biber Gürültüsünde Tekrarsız Medyan Filtre

Yıl 2017, Cilt: 6 Sayı: 2, 11 - 19, 31.08.2017

Uğur Erkan Levent Gökrem

Öz

Bu
çalışmada lineer olmayan alçak geçiren bir filtre geliştirilmiştir. Bu yeni
yöntem yeni piksel değerine, pencere içerisinde piksellerden yeni bir küme
oluşturarak karar vermektedir. Yöntemin gürültü sonuçları tuz-biber
gürültüsünde test edilmiştir. Yöntemi karşılaştırmak için Peak Signal to Noise
Ratio (PSNR) ve Structural Similarity (SSIM) ölçütleri kullanılmıştır. Yeni
geliştirilen yöntem Median Filtre (MF) ve Adaptive Median Filtre (AMF) ile
karşılaştırılmıştır. Karşılaştırma için 18 adet test görüntüsü kullanılmıştır.
Örneğin lena görüntüsü için, tuz-biber gürültü yoğunluğu %30 olduğunda MF ve
AMF’nin PSNR sonuçları 23.32, 25.22 çıkarken yeni yöntemde 31.29 çıkmıştır. Yeni
geliştirilen yöntem 18 adet test görüntüsüne ait tüm PSNR sonuçlarında diğer
yöntemlerden daha başarılı olmuştur. 

Anahtar Kelimeler

Tuz-biber gürültüsü Medyan Filtre Alçak geçiren filtre gürültü azaltma

Kaynakça

  • Ananthi, V.P. ve Balasubramaniam, P., 2016. A new image denoising method using interval-valued intuitionistic fuzzy sets for the removal of impulse noise, Signal Processing 121 (2016) 81–93.
  • Azimirad, E. ve Haddadnia J., 2015. Design of a new filtering for the noise removing in images by fuzzy logic, Journal of Intelligent & Fuzzy Systems 28, 1869–1876.
  • Chan, R. H., Ho, C. W. ve Nikolova, M., 2005. Salt-and-Pepper Noise Removal by Median-Type Noise Detectors and Detail-Preserving Regularization, IEEE Transactions on Image Processing, Vol. 14, No. 10, October 2005.
  • Cho, T. S., Zitnick, C. L., Joshi, N., Kang, S. B., Szeliski, R. ve Freeman W. T., 2012. Image Restoration by Matching Gradient Distributions, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 4, April.
  • Erkan, U. ve Kilicman, A, 2016. Two new methods for removing salt-and-pepper noise from digital images, ScienceAsia 42 : 28–32.
  • Goossens, B., Pizurica, A. ve Philips, W., 2009. Removal of Correlated Noise by Modeling the Signal of Interest in the Wavelet Domain, IEEE Transactions on Image Processing, Vol. 18, No. 6, June.
  • Gonzalez, R. C. ve Woods, R. E., 2008. Digital Image Processing (3rd Edition).
  • Jiang J,, Zhang L. ve Yang J., 2014. Mixed Noise Removal by Weighted Encoding With Sparse Nonlocal Regularization, IEEE Transactions on Image Processing, Vol. 23, No. 6, June.
  • Jin, L., Zhu, Z., Xu, X. ve Li, X., 2016. Two-stage quaternion switching vector filter for color impulse noise removal, Signal Processing 128, 171–185.
  • Lin C. H., Tsai, J.-S. ve Chiu, C. T., 2010. Switching Bilateral Filter with a Texture/Noise Detector for Universal Noise Removal, IEEE Transactıons on Image Processing, Vol. 19, No. 9, September.
  • Mäkitalo, M. ve Foi, A., 2013. Optimal Inversion of the Generalized Anscombe Transformation for Poisson-Gaussian Noise, IEEE Transactions on Image Processing, Vol. 22, No. 1, January 2013.
  • Morillas, S., Gregori, V., Peris-Fajarne´s, G., Sapena, A., 2008. Local self-adaptive fuzzy filter for impulsive noise removal in color images, Signal Processing 88, 390–398.
  • Morillas, S., Gregori, S. V. ve Hervás, A., 2009. Fuzzy Peer Groups for Reducing Mixed Gaussian-Impulse Noise from Color Images, IEEE Transactions on Image Processing, Vol. 18, No. 7, July.
  • Nguyen, M.P. ve Chun, S.Y., 2017. Bounded Self-Weights Estimation Method for Non-Local Means Image Denoising Using Minimax Estimators, IEEE Trans. Image Process., vol. 26, No. 4, Apr.
  • Shrestha, S. (2014). Image Denoising Using New Adaptive Based Medıan Filter, Signal & Image Processing: An International Journal (SIPIJ) Vol.5, No.4, August.
  • Srinivasan, K. S. ve Ebenezer, D., 2007. A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises, IEEE Signal Processing Letters, Vol. 14, No. 3, March.
  • Sulaiman, S. N., Isa, N. A. M, Yusoff, I. A. ve Ahmad F., 2015. Switching-based clustering algorithms for segmentation of low-level salt-and-pepper noise–corrupted images, SIViP 9:387–398.
  • Thanh, D.N.H., Dvoenko, S.D., 2016. A Method of Total Variation to Remove the Mixed Poisson-Gaussian Noise, Pattern Recognition and Image Analysis, vol. 26, no. 2, pp. 285–293.
  • Yuan, C. ve Li Y., 2015. Switching median and morphological filter for impulse noise removal from digital images, Optik 126, 1598–1601.
  • Zhang, P. ve Li, F., 2014. A New Adaptive Weighted Mean Filter for Removing Salt-and-Pepper Noise, IEEE Signal Processing Letters, Vol. 21, No. 10, October 2014.
  • Zhou, W., Bovik, A.C., Sheikh, H.R. ve E. P., 2004. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Trans. Image Process., Vol. 13, No. 4, Apr.

Yıl 2017, Cilt: 6 Sayı: 2, 11 - 19, 31.08.2017

Uğur Erkan Levent Gökrem

Öz

Kaynakça

  • Ananthi, V.P. ve Balasubramaniam, P., 2016. A new image denoising method using interval-valued intuitionistic fuzzy sets for the removal of impulse noise, Signal Processing 121 (2016) 81–93.
  • Azimirad, E. ve Haddadnia J., 2015. Design of a new filtering for the noise removing in images by fuzzy logic, Journal of Intelligent & Fuzzy Systems 28, 1869–1876.
  • Chan, R. H., Ho, C. W. ve Nikolova, M., 2005. Salt-and-Pepper Noise Removal by Median-Type Noise Detectors and Detail-Preserving Regularization, IEEE Transactions on Image Processing, Vol. 14, No. 10, October 2005.
  • Cho, T. S., Zitnick, C. L., Joshi, N., Kang, S. B., Szeliski, R. ve Freeman W. T., 2012. Image Restoration by Matching Gradient Distributions, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 4, April.
  • Erkan, U. ve Kilicman, A, 2016. Two new methods for removing salt-and-pepper noise from digital images, ScienceAsia 42 : 28–32.
  • Goossens, B., Pizurica, A. ve Philips, W., 2009. Removal of Correlated Noise by Modeling the Signal of Interest in the Wavelet Domain, IEEE Transactions on Image Processing, Vol. 18, No. 6, June.
  • Gonzalez, R. C. ve Woods, R. E., 2008. Digital Image Processing (3rd Edition).
  • Jiang J,, Zhang L. ve Yang J., 2014. Mixed Noise Removal by Weighted Encoding With Sparse Nonlocal Regularization, IEEE Transactions on Image Processing, Vol. 23, No. 6, June.
  • Jin, L., Zhu, Z., Xu, X. ve Li, X., 2016. Two-stage quaternion switching vector filter for color impulse noise removal, Signal Processing 128, 171–185.
  • Lin C. H., Tsai, J.-S. ve Chiu, C. T., 2010. Switching Bilateral Filter with a Texture/Noise Detector for Universal Noise Removal, IEEE Transactıons on Image Processing, Vol. 19, No. 9, September.
  • Mäkitalo, M. ve Foi, A., 2013. Optimal Inversion of the Generalized Anscombe Transformation for Poisson-Gaussian Noise, IEEE Transactions on Image Processing, Vol. 22, No. 1, January 2013.
  • Morillas, S., Gregori, V., Peris-Fajarne´s, G., Sapena, A., 2008. Local self-adaptive fuzzy filter for impulsive noise removal in color images, Signal Processing 88, 390–398.
  • Morillas, S., Gregori, S. V. ve Hervás, A., 2009. Fuzzy Peer Groups for Reducing Mixed Gaussian-Impulse Noise from Color Images, IEEE Transactions on Image Processing, Vol. 18, No. 7, July.
  • Nguyen, M.P. ve Chun, S.Y., 2017. Bounded Self-Weights Estimation Method for Non-Local Means Image Denoising Using Minimax Estimators, IEEE Trans. Image Process., vol. 26, No. 4, Apr.
  • Shrestha, S. (2014). Image Denoising Using New Adaptive Based Medıan Filter, Signal & Image Processing: An International Journal (SIPIJ) Vol.5, No.4, August.
  • Srinivasan, K. S. ve Ebenezer, D., 2007. A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises, IEEE Signal Processing Letters, Vol. 14, No. 3, March.
  • Sulaiman, S. N., Isa, N. A. M, Yusoff, I. A. ve Ahmad F., 2015. Switching-based clustering algorithms for segmentation of low-level salt-and-pepper noise–corrupted images, SIViP 9:387–398.
  • Thanh, D.N.H., Dvoenko, S.D., 2016. A Method of Total Variation to Remove the Mixed Poisson-Gaussian Noise, Pattern Recognition and Image Analysis, vol. 26, no. 2, pp. 285–293.
  • Yuan, C. ve Li Y., 2015. Switching median and morphological filter for impulse noise removal from digital images, Optik 126, 1598–1601.
  • Zhang, P. ve Li, F., 2014. A New Adaptive Weighted Mean Filter for Removing Salt-and-Pepper Noise, IEEE Signal Processing Letters, Vol. 21, No. 10, October 2014.
  • Zhou, W., Bovik, A.C., Sheikh, H.R. ve E. P., 2004. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Trans. Image Process., Vol. 13, No. 4, Apr.
Toplam 21 adet kaynakça vardır.

Ayrıntılar

Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Uğur Erkan

Levent Gökrem

Yayımlanma Tarihi 31 Ağustos 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 6 Sayı: 2

Kaynak Göster

APA Erkan, U., & Gökrem, L. (2017). Tuz-Biber Gürültüsünde Tekrarsız Medyan Filtre. Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(2), 11-19.
AMA Erkan U, Gökrem L. Tuz-Biber Gürültüsünde Tekrarsız Medyan Filtre. GBAD. Ağustos 2017;6(2):11-19.
Chicago Erkan, Uğur, ve Levent Gökrem. “Tuz-Biber Gürültüsünde Tekrarsız Medyan Filtre”. Gaziosmanpaşa Bilimsel Araştırma Dergisi 6, sy. 2 (Ağustos 2017): 11-19.
EndNote Erkan U, Gökrem L (01 Ağustos 2017) Tuz-Biber Gürültüsünde Tekrarsız Medyan Filtre. Gaziosmanpaşa Bilimsel Araştırma Dergisi 6 2 11–19.
IEEE U. Erkan ve L. Gökrem, “Tuz-Biber Gürültüsünde Tekrarsız Medyan Filtre”, GBAD, c. 6, sy. 2, ss. 11–19, 2017.
ISNAD Erkan, Uğur - Gökrem, Levent. “Tuz-Biber Gürültüsünde Tekrarsız Medyan Filtre”. Gaziosmanpaşa Bilimsel Araştırma Dergisi 6/2 (Ağustos 2017), 11-19.
JAMA Erkan U, Gökrem L. Tuz-Biber Gürültüsünde Tekrarsız Medyan Filtre. GBAD. 2017;6:11–19.
MLA Erkan, Uğur ve Levent Gökrem. “Tuz-Biber Gürültüsünde Tekrarsız Medyan Filtre”. Gaziosmanpaşa Bilimsel Araştırma Dergisi, c. 6, sy. 2, 2017, ss. 11-19.
Vancouver Erkan U, Gökrem L. Tuz-Biber Gürültüsünde Tekrarsız Medyan Filtre. GBAD. 2017;6(2):11-9.