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Enhancing Images Ch 5:Shapiro, Ch 3:Gonzales

Enhancing Images Ch 5:Shapiro, Ch 3:Gonzales. Gray level Mapping. Brightness Transform: 1. Position Dependent f(i,j)= g(i,j). e(i,j) g:Clean image e:position dependent noise 2. Position independent. 2. Position Independent Gray Level Mapping s =T( r ). Negation.

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Enhancing Images Ch 5:Shapiro, Ch 3:Gonzales

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  1. Enhancing ImagesCh 5:Shapiro, Ch 3:Gonzales

  2. Gray level Mapping Brightness Transform: 1. Position Dependent f(i,j)= g(i,j). e(i,j) g:Clean image e:position dependent noise 2. Position independent

  3. 2. Position Independent Gray Level Mapping s=T(r)

  4. Negation

  5. 2. Gamma Transformation s=T(r)

  6. Gamma Correction of CRT

  7. Image Enhancement by Gamma Transform: s=c.rɣ

  8. Image Enhancement by Gray level mapping: s=c.rɣ

  9. Image Enhancement by Contrast Stretching

  10. Image Enhancement by Gray level mapping

  11. HİSTOGRAM PROCESSİNG: H(rk)=nk rk: kth gray level, nk: number of pixels with gray value rk

  12. Histogram Equalization Goal: Find a transformation which yields a histogram with uniform density ?

  13. Algorithm: Histogram Equalization • Create an array h with L gray values • Initialize with o value • Find the histogram h(rk)= h(rk)+1 • Find the cumulative histogram hc(rk)= hc(rk-1)+ hc(rk) • Set T(rk-1) =round [{(L-1)/NM}. hc(rk-1)] • Create the equalized image, sk= T(rk)

  14. Histogram Equalization

  15. Equalized Histogram

  16. Histogram Specification

  17. Histogram Modification

  18. Histogram of a dark image

  19. Histogram Equalization

  20. Specified Histogram

  21. Local Histogram Equalization

  22. Image Subtraction

  23. Convolution or crosscorrelation

  24. Position Dependent Gray Level Mapping Use convolution or correlation: f*h

  25. Define a mask and correlate it with the image

  26. SMOOTHING

  27. Image Enhancement WITH SMOOTING

  28. Averaging blurrs the image

  29. Image Enhancement WITH AVERAGING AND THRESHOLDING

  30. Apply averaging to only pixels with brightness value outside a predefined interval. Mask h(i,j) = Restricted Averaging • For g(m+i,n+j)€ [min, max] • 0 otherwise Q: Study edge strenght smoothing, inverse gradient and rotating mask

  31. Median Filtering • Find a median value of a given neighborhood. • Removes sand like noise 0 1 1 2 2 2 2 3 3

  32. Median filtering breaks the straight lines Square filter: 0 0 0 5 5 5 5 5 5 Cross filter 0 0 0 5 5

  33. Image Enhancement with averaging and median filtering

  34. EDGE PROFILES Edges are the pixels where the brightness changes abrubtly. It is a vector variable with magnitude and direction

  35. EDGES, GRADIENT AND LAPLACIAN

  36. SMOOT EDGES, NOISY EDGES

  37. Gradient Δg(x,y) = ∂g/ ∂x + ∂g/ ∂y Magnitude: |Δg(x,y) | = √ (∂g/ ∂x)2 + (∂g/ ∂y) 2 Phase : Ψ = arg (∂g/ ∂x , ∂g/ ∂y) radians Continuous world

  38. Use difference in various directions Δi g(i,j) = g(i,j) - g(i+1,j) or Δj g(i,j) = g(i,j) - g(i,j+1) or Δij g(i,j) = g(i,j)- g(i+1,j+1) or |Δ g(i,j) | = |g(i,j)- g(i+1,j+1) | + |g(i,j+1)- g(i+1,j) | Discrete world

  39. GRADIENT EDGE MASKS Approximation in discrete grid

  40. GRADIENT EDGE MASKS

  41. GRADİENT MASKS

  42. GRADİENT MASKS

  43. GRADİENT MASKS

  44. GRADİENT MASKS

  45. Edge Detection

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