Gradyan Anahtarlamalı Gauss Görüntü Filtresi

Abstract

Gürültü görüntü işleme tekniklerinin başarısını etkileyen en önemli faktörlerden biridir. Görüntü işleme tekniklerinin başarısını arttırabilmek için gürültünün azaltılması gerekmektedir. Gürültüyü azaltabilmek için görüntülere filtreleme işlemi uygulanmaktadır. Sunulan bu çalışmada, görüntülerdeki karışık gürültüyü giderebilmek için filtre tasarımı yapılmıştır. Görüntüye ilk olarak uyarlamalı medyan filtresi uygulanmış ve görüntüde tespit edilen tuz ve biber gürültüsünün giderilmesi amaçlanmıştır. Tuz ve biber gürültüsü bulunmayan piksellere ise anahtarlamalı Gauss filtresi uygulanmıştır. Tasarlanan anahtarlamalı filtrede Gauss filtresine ait parametre kullanıcı müdahalesi olmadan otomatik olarak belirlenmiştir. Parametrenin belirlenmesinde görüntünün gradyan bilgisi ve eşik değer bilgisinden yararlanılmıştır. Bu amaca yönelik olarak da MATLAB Grafik Kullanıcı Arayüzü (GKA) tasarlanmıştır. GKA yardımıyla tasarlanan filtrenin uygulama sonuçları kameraman ve Lena görüntüleri üzerinde sunulmuştur.

Keywords

• Gürültü
• Gauss filtresi
• Uyarlamalı Medyan filtre
• Görüntü işleme

Gradient Switched Gaussian Image Filter

Abstract

Noise is one of the most important factor that affects the success of image processing techniques. Reduction of the noise is needed to improve the success of image processing techniques. Filters are applied to the images to reduce the noise. In this study, a filter was designed to reduce the mixed noise on the image. Initially, adaptive median filter was applied to the image and reduction of the detected salt and pepper noise was aimed. Switching Gaussian filter was applied to the pixels which did not have salt and pepper noise. In the designed switching filter, parameter of the Gauss filter is determined automatically without user intervention. The gradient information and the threshold value information were used for determining the parameter. For this purpose, a Graphical User Interface (GUI) was designed with MATLAB. The application results of the designed filter were presented on the Cameraman and Lena images with the help of GUI.

Keywords

• Noise
• Gaussian filter
• Adaptive median filter
• Image processing
• MATLAB GUI

References

1. [1] R.C. Gonzales, R. E. Woods, “Digital Image Processing,” 3rd Edition, Addison Wesley, MA, 2002.
2. [2] R. Szeliski, “Computer Vision: Algorithms and Applications,” Springer, 2010.
3. [3] G. Yan, Q. Pan, and Y. Kang, “Research on a New Gaussian Self-adaptive Smoothing Algorithm in Image Processing,” Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology, 2005, pp. 348-352.
4. [4] T. A. Nguyen, W. S.Song, and M. C.Hong, “Spatially Adaptive Denoising Algorithm for a Single Image Corrupted by Gaussian Noise,” IEEE Transactions on Consumer Electronics, vol. 56, no. 3, pp. 1610-1615, 2010.
5. [5] K. Celi̇k, H. H. Sayan ve R. Demirci, “Gradyan Uyarlamalı Gauss Görüntü Filtresi,” 23nd Signal Processing and Communications Applications Conference (SIU), Malatya, Turkey, 2015, ss. 879-882.
6. [6] A. Toprak and İ. Güler, “Impulse Noise Reduction in Medical Images with The Use of Switch Mode Fuzzy Adaptive Median Filter,” Digital Signal Processing, vol. 17, no. 4, pp. 711-723, 2007.
7. [7] X. Zhang and Y. Xiong, “Impulse Noise Removal Using Directional Difference Based Noise Detector and Adaptive Weighted Mean Filter,” IEEE Signal Processing Letters, vol. 16, no. 4, pp. 295-298, 2009.
8. [8] S. Akkoul, R. Ledee, R. Leconge, and R. Harba, “A New Adaptive Switching Median Filter,” IEEE Signal Processing Letters, vol. 17, no. 6, pp. 587-590, 2010.

9. [9] R. Ha, P. Liu, and K. Jia, “An Improved Adaptive Median Filter Algorithm and Its Application,” Advances in Intelligent Information Hiding and Multimedia Signal Processing, pp. 179-186, doi: 10.1007/978-3-319-50212-0_22
10. [10] T. Li, X. Zhang, and C. Li, “An Improved Adaptive Image Filter for Edge and Detail Information Preservation,” International Conference on Systems and Informatics (ICSAI2012), Yantai, 2012, pp. 1870-1873.
11. [11] X. Ji, P. Yuan, Z. Shi, J. Li, T. Wang, S. Cao, and L. Gao, “An Effective Self-Adaptive Mean Filter for Mixed Noise,” International Conference on Advanced Robotics and Mechatronics (ICARM), Macau, 2016, pp. 484-489.
12. [12] N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66, 1979.
13. [13] M. Sezgin and B. Sankur, “Survey over Image Thresholding Techniques and Quantitative Performance Evaluation,” Journal of Electronic Imaging, vol. 13, no. 1, pp.146-168, 2004.
14. [14] P. D. Sathya and R. Kayalvizhi, “Amended Bacterial Foraging Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Images,” Measurement, vol. 44, no. 10, pp. 1828-1848, 2011.
15. [15] E. Abreu, M. Lightstone, S.K. Mitra, and K. Arakawa, “A New Efficient Approach for The Removal of Impulse Noise from Highly Corrupted Images,” IEEE Transactions on Image Processing, Vol. 5, No. 6, pp. 1012-1025, 1996.
16. [16] O. T. Holland, and P. Marchand, “Graphics and GUIs with MATLAB,” 3 edition, Chapman and Hall/CRC, 2002
17. [17] MATLAB, “Creating Graphical User Interfaces,” The Matworks, Inc., R2014a
18. [18] İ. Çankaya, D. Akgün ve S. Kaçar, “Mühendislik Uygulamaları için MATLAB”, Seçkin Yayıncılık, Turkey, 2016.
19. [19] Ş. Şahan, A. Değirmenci, and İ. Çankaya, “A Study on Clustering Based Image Thresholding Techniques with MATLAB GUI,” International Journal of Engineering Science and Computing, vol. 13, no. 1, pp. 146-168, 2016.