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Computer Vision – Enhancement(Part II)

Computer Vision – Enhancement(Part II). Hanyang University Jong-Il Park. Local Enhancement. Global enhancement The same operation for all pixels Local enhancement Different operation for each pixel According to the statistics of local support. Local Histogram Equalization.

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Computer Vision – Enhancement(Part II)

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  1. Computer Vision – Enhancement(Part II) Hanyang University Jong-Il Park

  2. Local Enhancement • Global enhancement • The same operation for all pixels • Local enhancement • Different operation for each pixel • According to the statistics of local support

  3. Local Histogram Equalization • Using a fixed window at each point • Computationally expensive • Histogram equalization at each point

  4. Use of statistics of local support • Eg. m s E Enhanced image Original image

  5. Input image output * Spatial mask ( 33, 55, ) Spatial Operations • Spatial averaging and spatial LPF for noise smoothing

  6. Spatial Mask

  7. Spatial Averaging • Mean-filtering • Noise reduction

  8. Spatial Averaging Mask • Spatial averaging masks a(k,l) • Disadvantage : blurring

  9. Effect of window size

  10. Eg. Spatial Averaging(1)

  11. Eg. Spatial Averaging(2) Original image Averaging 후의 image

  12. Cf. Multi-imageaveraging

  13. Spatial Operations - Filtering • Parametric Low Pass Filter • but to preserve the mean

  14. Spatial LPF, BPF, HPF Spatial averaging LPF + (a) Spatial low-pass filter (b) Spatial high-pass filter LPF + LPF (c) Spatial band-pass filter

  15. Eg. Spatial LPF Original image Lowpass Filter된 후의 image

  16. Spatial High-Pass Filtering

  17. Eg. Spatial HPF Original image Highpass filtered image

  18. Spatial Band-Pass Filtering Original image Lowpass Filter(Short Term) =A Bandpass Filter된 후의 Image =B-A Lowpass Filter(Long Term) =B

  19. Denoising by LPF Noisy! Blurred! Trade-off?

  20. l k Directional Smoothing • Directional Smoothing • to protect the edges from blurring while smoothing

  21. Eg. Directional Smoothing Original image LowpassFilter(LongTerm) Direc.Smoothing (대각선) Direc. Smoothing (수 직)

  22. Median Filtering • Median Filter • Properties • nonlinear filter • Example

  23. Eg. 1D Median Filtering

  24. Discussion – Median filter 1) median filter preserve discontinuities in a step function 2) smooth a few pixels whose values differ significantly from the surrounding, without affecting the other pixels. 3) pulse function, whose width is less than one half the filter length, are suppressed

  25. 2D Median Filtering Filter Filtered Image Original Image Filter Filtered Image

  26. Eg. Median Filtering Salt-and-pepper noise(=impulsive noise) 7x7 Median filtered image Original  Excellent performance!

  27. Eg. Median Filter – Impulsive Noise

  28. Eg. Median Filter – Impulsive Noise

  29. Eg. Median Filter – Gaussian Noise Moderate performance

  30. Various patterns for median filter Neighborhood patterns used for median filtering

  31. Eg. Median filter – Square pattern Original image 10% black, 10% white Median filtering using 5 by 5 square region Median filtering using 3 by 3 square region

  32. Eg. Median filter – Octagon pattern Original image 5 by 5 octagonal median filter

  33. Eg. Median filter – Reconstruction Original image Median filtering and color compensation

  34. Sharpening Images • Emphasis of high-frequency components • Usually exploiting 1st order derivative and 2nd order derivatives • 1D derivatives • 1st order derivative: • 2nd order derivative:

  35. Eg. 1st & 2nd order derivatives

  36. Observation on derivatives • 2nd order derivative • Thinner edges • Stronger response to fine details • Weaker response to a gray-level step • Double response at step changes • Intensity of response: point > line > step • The 2nd order derivative is better suited than the 1st order derivative for image enhancement.

  37. Laplacian Operator – Derivation • The simplest isotropic derivative operator

  38. Laplacian Operator

  39. Sharpening by Laplacian operator

  40. Eg. Sharpening Subtraction of the Laplacian from the original Original SEM image Laplacian operator Subtraction of the Laplacian from the original Original image Laplacian operator

  41. Composite Laplacian mask

  42. (3) (1) Signal High-pass (2) (1)+(3) Low-pass Unsharp masking and Crispening

  43. Unsharp mask application Original image Processed image

  44. High-boost filtering Let g(n1, n2) = u(n1, n2) - uL(n1, n2) v(n1, n2) = u(n1, n2) + k g(n1, n2) • k=1: Unsharp Masking • Crispening an image • k>1: High-boost filtering • edge or line details to be emphasized

  45. Eg. High-boost filtering

  46. 1 1 1 1 Zoom(1:2 magnification) revisited • Nearest neighbor=Replication = zero - order hold column, row zero-padding

  47. Zoom revisited(cont.) • Linear Interpolation : first - order hold

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