Median Filtering

1. Median Filtering In signal processing, it is often desirable to be able to perform some kind of noise reduction on an image or signal. The median filter is a nonlinear digital filtering technique, often used to remove noise. Median filtering is very widely used in digital image processing because it preserves edges while removing noise. • Course Name: Digital Image Processing Level(UG/PG): UG • Author(s) : Phani Swathi Chitta • Mentor: Prof. Saravanan Vijayakumaran *The contents in this ppt are licensed under Creative Commons Attribution-NonCommercial-ShareAlike 2.5 India license

2. Learning Objectives After interacting with this Learning Object, the learner will be able to: • Explain the reduction of noise using a median filter

3. Definitions of the components/Keywords: 1 • The median filter is a sliding-window spatial filter. • It replaces the value of the center pixel with the median of the intensity values in the neighborhood of that pixel. • Median filtering is a nonlinear operation often used in image processing to reduce "salt and pepper" noise. A median filter is more effective than convolution when the goal is to simultaneously reduce noise and preserve edges. • Median filters are particularly effective in the presence of impulse noise, also called ‘salt – and – pepper’ noise because of its appearance as white and black dots superimposed on an image. • For every pixel, a 3x3 neighborhood with the pixel as center is considered. In median filtering, the value of the pixel is replaced by the median of the pixel values in the 3x3 neighborhood. 2 3 4 5

4. Master Layout 1 Original Image Image with ‘Salt & pepper’ noise Image after filtering 2 3 4 • Give a slider ranging from 0 to 1 so that user can select any one value of noise density. 5

5. Step 1: Noise density 0.01 1 2 3 4 5

6. Step 2: Noise density 0.02 1 2 3 4 5

7. Step 3: Noise density 0.05 1 2 3 4 5

8. Step 4: Noise density 0.07 1 2 3 4 5

9. Step 5: Noise density 0.09 1 2 3 4 5

10. Step 6: Noise density 0.1 1 2 3 4 5

11. Step 7: Noise density 0.15 1 2 3 4 5

12. Step 8: Noise density 0.2 1 2 3 4 5

13. Step 9: Noise density 0.22 1 2 3 4 5

14. Step 10: Noise density 0.3 1 2 3 4 5

15. Step 11: Noise density 0.4 1 2 3 4 5

16. Step 12: Noise density 0.5 1 2 3 4 5

17. Step 13: Noise density 0.6 1 2 3 4 5

18. Step 14: Noise density 0.7 1 2 3 4 5

19. Step 15: Noise density 0.8 1 2 3 4 5

20. Step 16: Noise density 0.9 1 2 3 4 5

21. Step 17: Noise density 1 1 2 3 4 5

22. Electrical Engineering Slide 1 Slide 3 Slide 23, 24,25 Slide 26 Introduction Definitions Analogy Test your understanding (questionnaire)‏ Lets Sum up (summary)‏ Want to know more… (Further Reading)‏ Interactivity: Try it yourself • Select any one of the figures • a b • c d • Select the amount of noise density 22 Credits

23. Questionnaire 1 1.What is the value of the yellow box after median filtering? Answers: a) 7 b) 1 c) 3 d)‏ 9 2 3 4 5

24. Questionnaire 1 2. What are the values of yellow boxes after median filtering is done? Answers: a) b) 2 3 4 5

25. Questionnaire 1 2. What are the values of yellow boxes after median filtering is done? Answers: c) d) 2 3 4 5

26. Links for further reading Reference websites: http://en.wikipedia.org/wiki/Median_filter http://medim.sth.kth.se/6l2872/F/F7-1.pdf Books: Digital Image Processing – Rafael C. Gonzalez, Richard E. Woods, Third edition, Prentice Hall