Enhancing Images Ch 5:Shapiro, Ch 3:Gonzales

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 ?

14. 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)

15. Histogram Equalization

18. Equalized Histogram

19. Histogram Specification

20. Histogram Modification

21. Histogram of a dark image

22. Histogram Equalization

23. Specified Histogram

24. Local Histogram Equalization

25. Image Subtraction

26. Convolution or crosscorrelation

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

28. Define a mask and correlate it with the image

29. SMOOTHING

30. Image Enhancement WITH SMOOTING

31. Averaging blurrs the image

33. Image Enhancement WITH AVERAGING AND THRESHOLDING

34. 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

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

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

38. Image Enhancement with averaging and median filtering

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

40. EDGES, GRADIENT AND LAPLACIAN

41. SMOOT EDGES, NOISY EDGES

42. 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

43. 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

44. GRADIENT EDGE MASKS Approximation in discrete grid

45. GRADIENT EDGE MASKS

46. GRADİENT MASKS

47. GRADİENT MASKS

48. GRADİENT MASKS

49. GRADİENT MASKS

50. Edge Detection