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Chapter 9

Chapter 9. Spatial Filters Blurring Median Filtering Hi-pass filtering SpatialFilterFrame and JAI. Blurring=Low-pass Filters. Why Blur an Image?. Remove Noise Other ideas?. Average=low-pass. Want Unity Gain. Many variations on LP filters. Gaussian Blur. Why use Gaussian Blur?.

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Chapter 9

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  1. Chapter 9 • Spatial Filters • Blurring • Median Filtering • Hi-pass filtering • SpatialFilterFrame and JAI

  2. Blurring=Low-pass Filters

  3. Why Blur an Image? • Remove Noise • Other ideas?

  4. Average=low-pass

  5. Want Unity Gain

  6. Many variations on LP filters

  7. Gaussian Blur

  8. Why use Gaussian Blur? • How the eye works • Symmetric • Differentiable • Smooth

  9. Classic bell curve

  10. Larger Kernels=more blur

  11. Median Filtering • Middle of a list of samples listed in ascending order. • Sort samples, return n/2

  12. Why use median filtering? • Discard outliers • 0, 85, 90, 87 and 100. The mean is 72 • Median is 87. • {0, 85, 87, 90, and 100}=87

  13. How do I implement the median?

  14. Salt and Pepper

  15. Median 3x3

  16. Median on any kernel

  17. Median result

  18. Median Octagon, less aggressive

  19. Median Octagon results

  20. Median Filtering is not FREE! • Image degradation • Selective median filtering. • How do you know when to apply it?

  21. Use Sample Variance

  22. Coefficient of variation public static boolean outlierHere(int a[]) { return ( coefficientOfVariation(a) > .4); }

  23. Before and After 3x3 Median Filtering with Outlier Detection

  24. High-pass Filters

  25. Defining HP Kernels public void hp1() { float k[][] = { { 0, -1, 0}, {-1, 10, -1}, { 0, -1, 0} }; Mat.normalize(k); convolve(k); }

  26. Lower Center Number, GREATER edge enhancement! 5 10

  27. Project ideas Integrate the Kernels of math.Mat2.Kernels into the MDI interface. Use j2d.ImageUtils.convolution to implement your operations

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