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cs489 02 cs589 02 multimedia processing lecture 2 intensity transformation and spatial filtering n.
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  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