Image enhancement techniques - Digital Image Processing

PPT

1. Image Enhancement Definition Image Enhancement: is the process that improves the quality of the image for a specific application

2. Image Enhancement Methods Spatial Domain Methods (Image Plane) Techniques are based on direct manipulation of pixels in an image Frequency Domain Methods Techniques are based on modifying the Fourier transform of the image. Combination Methods There are some enhancement techniques based on various combinations of methods from the first two categories

3. Topic for Learning through evocation 5

4. Spatial Domain Methods As indicated previously, the term spatial domain refers to the aggregate of pixels composing an image. g(x,y) = T [f(x,y)] Where f(x,y) in the input image, g(x,y) is the processed image and T is as operator on f, defined over some neighborhood of (x,y)

5. What is the purpose of road roller? 7

6. How it is used to smoothen the road?? levelling equalize 8

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10. Smoothing Capture important patterns in the data Leaving out noise or other fine-scale structures 12

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13. Formative assessment questions I 1. State the effect of smoothing filter in spatial domain. (R/F) a) reduce the noise present in image b) reduce fine details of image c) Reduce the intensity of image d) both (a) and (b) 2. Compute the intensity range of 9 level grey image. ( Ap /C) a) 0 to 255 b) 0 to 512 c) -256 to 255 d) 0 to 128 15

14. Smoothing linear filters Averaging filters Box filter Weighted average filter 16

15. The general implementation for filtering an M X N image with a weighted averaging filter of size m x n is given by where a =( m -1)/2 and b =( n -1)/2 17

16. Smoothing Spatial Filtering 1 1 1 1 1 1 1 1 1 Origin x y Image f (x, y) e = 1/9* ( 1 *106 + 1 *104 + 1 *100 + 1 *108 + 1 *99 + 1 *98 + 1 *95 + 1 *90 + 1 *85) = 98.3333 Filter Simple 3*3 Neighbourhood 106 104 99 95 100 108 98 90 85 1 / 9 1 / 9 1 / 9 1 / 9 1 / 9 1 / 9 1 / 9 1 / 9 1 / 9 3*3 Smoothing Filter 104 100 108 99 106 98 95 90 85 Original Image Pixels * 1/9 The above is repeated for every pixel in the original image to generate the smoothed image 18

17. 19 254 10 9 8 11 15 8 17 51 21 250 240 252 231 10 15 20 230 225 31 33 35 11 9 61 90 117 92 63 36 66 116 142 112 33 62 112 133 104 Original Image Average Filter 254 10 9 8 11 15 8 17 51 21 250 240 252 231 10 15 20 230 225 Append Zero Filtered image

18. Smoothing Spatial Filters Image smoothing with masks of various sizes 20

19. Non-linear filters Median filter 3X3 neibourhood (10,20,20,20, 15 ,20,20,25,100) Arrange it in ascending order (10,15,20,20, 20 , 20,20,25,100) Replacing 15 by 20 (center pixel) 21

20. Max filter : find out brightest point in an image & reducing pepper noise Min filter : find out darkest point in an image & reducing salt noise 22

21. Example 1 1 1 1 89 1 1 1 1 Median {1 1 1 1 1 1 1 1 89} Min { 1 1 1 1 1 1 1 1 89} Max {1 1 1 1 1 1 1 1 89 } 23

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26. Formative assessment questions II 1. Predict the intensity value of pepper noise in 6 level grey image. ( Ap /C) a)0 b)255 c)128 d)1 2. Find the mean, median, min and max of the sequence 1,1,2,3,5,6,6,8. ( Ap /C) a) 4,3,1,8 b) 4,4,1,8 c) 4,5,1,8 d) 32,5,1,8 28

27. 3. Compute the intensity value of salt noise pixels in 9 level grey image. [ Ap /C] a)0 b)255 c)128 d)512 29

28. Discussion 30

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30. Summary Spatial smoothing filter Basics of noise Spatial domain Spatial filtering Classification of spatial domain filter Smoothing linear filters Box filter Weighted average filter Convolution for filter operation Smoothing non-linear filters Median filter Min filter Max filter 32

31. Assessment through Analogy 1. Consider the below real time image of a child which has salt and pepper noise in it. Compare the two resultant images produced by applying mean and median filter. 33

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33. 2. The following image is affected by salt noise. “The salt noise will be completely removed even if the median filter is used instead of min filter”. Justify the above statement. 35

34. If the salt noise = 5% Noisy image Median Min 36

35. If the salt noise = 50% Noisy image Median Min 37

36. References Rafael C Gonzalez, Richard E Woods, Digital Image Processing, 3rd Edition, Pearson Education, 2009 William K Pratt, Digital Image Processing, John Willey, 2007 Milan Sonka , Vaclav Hlavac , Roger Boyle, Image Processing Analysis and Machine Vision, Thompson Learning, 2008 S.Jayaraman , S.Esakkirajan and T.Veerakumar , Digital Image Processing, Tata McGraw Hill Education Private Limited, 2009 Bhabatosh Chanda , D. Dutta Majumder , Digital Image Processing and Analysis, Prentice Hall of India, 2011 38