# Spring 2009 - PowerPoint PPT Presentation

Spring 2009

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Spring 2009

## Spring 2009

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##### Presentation Transcript

1. CS489-02 & CS589-02 Multimedia ProcessingLecture 2. Intensity Transformation and Spatial Filtering Spring 2009

2. Spatial Domain vs. Transform Domain • Spatial domain Image plane itself, directly process the intensity values of the image plane • Transform domain Process the transform coefficients, not directly process the intensity values of the image plane

3. Spatial Domain Process

4. Spatial Domain Process

5. Spatial Domain Process

6. Some Basic Intensity Transformation Functions

7. Image Negatives

8. Example: Image Negatives Small lesion

9. Log Transformations

10. Example: Log Transformations

11. Power-Law (Gamma) Transformations

12. Example: Gamma Transformations

13. Example: Gamma Transformations

14. Piecewise-Linear Transformations • Contrast Stretching — Expands the range of intensity levels in an image ― spans the full intensity range of the recording medium • Intensity-level Slicing — Highlights a specific range of intensities in an image

15. Highlight the major blood vessels and study the shape of the flow of the contrast medium (to detect blockages, etc.) Measuring the actual flow of the contrast medium as a function of time in a series of images

16. Bit-plane Slicing

17. Example

18. Example

19. Histogram Processing • Histogram Equalization • Histogram Matching • Local Histogram Processing • Using Histogram Statistics for Image Enhancement

20. Histogram Processing

21. Histogram Equalization

22. Histogram Equalization

23. Histogram Equalization

24. Histogram Equalization

25. Example

26. Example

27. Histogram Equalization

28. Example: Histogram Equalization Suppose that a 3-bit image (L=8) of size 64 × 64 pixels (MN = 4096) has the intensity distribution shown in following table. Get the histogram equalization transformation function and give the ps(sk) for each sk.

29. Example: Histogram Equalization

30. Example: Histogram Equalization

31. Question Is histogram equalization always good? No

32. Histogram Matching Histogram matching (histogram specification) —A processed image has a specified histogram

33. Histogram Matching

34. Histogram Matching: Procedure • Obtain pr(r) from the input image and then obtain the values of s • Use the specified PDF and obtain the transformation function G(z) • Mapping from s to z

35. Histogram Matching: Example Assuming continuous intensity values, suppose that an image has the intensity PDF Find the transformation function that will produce an image whose intensity PDF is

36. Histogram Matching: Example Find the histogram equalization transformation for the input image Find the histogram equalization transformation for the specified histogram The transformation function

37. Histogram Matching: Discrete Cases • Obtain pr(rj) from the input image and then obtain the values of sk, round the value to the integer range [0, L-1]. • Use the specified PDF and obtain the transformation function G(zq), round the value to the integer range [0, L-1]. • Mapping from sk to zq

38. Example: Histogram Matching Suppose that a 3-bit image (L=8) of size 64 × 64 pixels (MN = 4096) has the intensity distribution shown in the following table (on the left). Get the histogram transformation function and make the output image with the specified histogram, listed in the table on the right.

39. Example: Histogram Matching Obtain the scaled histogram-equalized values, Compute all the values of the transformation function G,

40. Example: Histogram Matching

41. Example: Histogram Matching Obtain the scaled histogram-equalized values, Compute all the values of the transformation function G, s1 s0 s2 s3 s5 s6 s7 s4

42. Example: Histogram Matching

43. Example: Histogram Matching

44. Example: Histogram Matching

45. Example: Histogram Matching