Tuz ve Biber Gürültü Kaldırma için Uyarlamalı Cesáro Ortalama Filtresi

Öz

Bu çalışmada, bir tuz ve biber gürültü (SPN) kaldırma yöntemi, yani Uyarlamalı Cesáro Ortalama Filtresi (ACmF) öneriyoruz ve bazı temel kavramları veriyoruz. Ardından, ACmF’yi gürültü yoğunluğu %10 ile %90 arasında değişen çeşitli test görüntülerine uyguluyoruz: 15 geleneksel test görüntüsü (Baboon, Boat, Bridge, Cameraman, Elaine, Flintstones, Hill, House, Lake, Lena, Living Room, Parrot, Peppers, Pirate, and Plane) ve TESTIMAGES veri tabanında verilen 40 test görüntüsü. Daha sonra, ACmF'yi Uyarlamalı Ağırlıklı Ortalama Filtresi (AWMF), Farklı Uygulamalı Medyan Filtresi (DAMF) ve Gürültü Uyarlamalı Bulanık Anahtarlama Medyan Filtresi (NAFSMF) gibi gelişmiş yöntemlerle karşılaştırıyoruz. Pik Sinyal Gürültü Oranı (PSNR) ve Yapısal Benzerlik (SSIM) sonuçları, ACmF'nin yukarıda belirtilen yöntemlerden daha iyi performans sergilediğini göstermektedir. Ayrıca, bu algoritmaların çalışma zamanlarını da karşılaştırıyoruz. Bu çalışma süresi sonuçları ACmF'nin DAMF dışındaki yöntemleri geride bıraktığını gösteriyor. Sonunda daha fazla araştırmaya olan ihtiyacı tartışıyoruz.

Anahtar Kelimeler

• Tuz ve biber gürültüsü,gürültü kaldırma,,lineer olmayan fonksiyonlar,,Cesáro Ortalama

Adaptive Cesáro Mean Filter for Salt-and-Pepper Noise Removal

Öz

In this study, we propound a salt-and-pepper noise (SPN) removal method, i.e. Adaptive Cesáro Mean Filter (ACmF), and provide some of its basic notions. We then apply ACmF to several test images whose noise densities range from 10% to 90%: 15 traditional test images (Baboon, Boat, Bridge, Cameraman, Elaine, Flintstones, Hill, House, Lake, Lena, Living Room, Parrot, Peppers, Pirate, and Plane) and 40 test images, provided in the TESTIMAGES Database. Afterwards, we compare ACmF with the state-of-art methods, such as Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), and Noise Adaptive Fuzzy Switching Median Filter (NAFSMF). The results by The Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) show that ACmF performs better than the methods mentioned above. Moreover, we also compare the running time data of these algorithms. These results show that ACmF outperforms the methods except for DAMF. We finally discuss the need for further research.

Anahtar Kelimeler

• Salt-and-pepper noise,noise removal,,non-linear functions,image denoising,,Cesáro mean

Kaynakça

1. [1] Erkan, U., Serdar, E., Dang N.H., T., A Recursive Mean Filter for Image Denoising, in ‘Proceeding of IEEE 2019 International Conference on Artificial Intelligence and Data Processing’ (2019)
2. [2] H. Thanh, D.N., Thi Thanh, L., Surya Prasath, V.B., Erkan, U., An Improved BPDF Filter for High Density Salt and Pepper Denoising, in ‘2019 IEEE-RIVF International Conference on Computing and Communication Technologies (RIVF)’ (IEEE, 2019), pp. 1–5
3. [3] Erkan, U., Gökrem, L., Enginoğlu, S., Adaptive Right Median Filter for Salt-and-Pepper Noise Removal, Int. J. Eng. Res. Dev., 2019, 11(2), pp. 542–550.
4. [4] Tukey, J.W., Exploratory Data Analysis, Reading, MA: AddisonWesley, (1977).
5. [5] W. K. Pratt,Semiannual Technical Report, (Image Processing Institute, University of Southern California, 1975)
6. [6] Hwang, H., Haddad, R.A., Adaptive Median Filters: New Algorithms and Results, IEEE Trans. Image Process., 1995, 4(4), pp. 499–502.
7. [7] Erkan, U., Gökrem, L., Median Filter without Repetition in Salt and Peppers Noise, Gaziosmanpasa J. Sci. Res., 2017, 6(2), pp. 11–19.
8. [8] Wang, Z., Zhang, D., Progressive switching median filter for the removal of impulse noise from highly corrupted images, IEEE Trans. Circuits Syst. II Analog Digit. Signal Process., 1999, 46(1), pp. 78–80.

9. [9] Pattnaik, A., Agarwal, S., Chand, S., A New and Efficient Method for Removal of High Density Salt and Pepper Noise Through Cascade Decision based Filtering Algorithm, Procedia Technol., 2012, 6, pp. 108–117.
10. [10] Esakkirajan, S., Veerakumar, T., Subramanyam, A.N., PremChand, C.H., Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median Filter, IEEE Signal Process. Lett., 2011, 18(5), pp. 287–290.
11. [11] Toh, K.K. V., Isa, N.A.M., Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction, 2010, 17(3), pp. 281–284.
12. [12] Erkan, U., Kilicman, A., Two new methods for removing salt-and-pepper noise from digital images, ScienceAsia, 2016, 42, pp. 28–32.
13. [13] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P., Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process., 2004, 13(4), pp. 600–612.
14. [14] Erkan, U., Gökrem, L., A new method based on pixel density in salt and pepper noise removal, Turkish J. Electr. Eng. Comput. Sci., 2018, 26, pp. 162–171.
15. [15] Erkan, U., Gökrem, L., Enginoğlu, S., Different applied median filter in salt and pepper noise, Comput. Electr. Eng., 2018, 70, pp. 789–798.
16. [16] Zhang, P., Li, F., A new adaptive weighted mean filter for removing salt-and-pepper noise, IEEE Signal Process. Lett., 2014, 21(10), pp. 1280–1283.
17. [17] Asuni, N., Giachetti, A., (2014) TESTIMAGES: A Large-Scale Archive for Testing Visual Devices and Basic Image Processing Algorithms, STAG - Smart Tools & Apps for Graphics - Eurographics Italian Chapter Conference. The Eurographics Association. https://doi.org/10.2312/stag.20141242.