İmage enhancement

1. İmage enhancement Prepare image for further processing steps for specific applications

2. Image enhancement: Pre-processing • Spatial domain techniques: Find a transformation T f(x,y) g(x,y) • Frequency domain techniques • f(x,y) F(u,v) G(u,v) g(x,y) T F-1 F T

3. Image Enhancement in spatial domain • Brightness Transform: 1. Position Dependent f(i,j)= g(i,j). e(i,j) g:Clean image e:position dependent noise 2. Gray scale Transform

4. Gray scale transform: s=T(r) • r original color, s transformed color s S=r S=r L-1 S=r r L-1

5. Gray Scale Transformq=T(p)Binarize and contrast streching

6. Image Enhancement THRESHOLDING

7. Log Transform:q= clog (1+p)

8. Negation

9. Power law transform

10. Image Enhancement by Gray scale transform

11. Image Enhancement by Gray scale transform

12. Image Enhancement by Gray Scale Transform

13. Image Enhancement by Gray scale transform

14. Image Enhancement by Gray scale transform

15. Bit plane slicing • Soppose each pixel is represented by n-bits. • Represent each bit by a plane

16. Bit-plane slicing Image Enhancement in the Spatial Domain

17. Chapter 3 Image Enhancement in the Spatial Domain

18. Histogram processing • Given an image with L gray levels • h(rk) = nk • rk: kth gray level • nk: number of pixels with gray level rk • Normalized histogram P(rk) = nk/N N:total number of pixels

19. Histograms of various image

20. Histogram Equalization Find a transformation which yields a histogram with uniform density ?

21. Histogram of a dark image

22. Equalized Histogram

23. Specified Histogram

24. Local Histogram Equalization

25. Local ProcessingConvolution or Correlation: f*h

26. Define a mask and correlate it with the image

27. SMOOTHING

28. Image Enhancement WITH SMOOTING

29. Averaging blurrs the image

30. Image Enhancement WITH AVERAGING AND THRESHOLDING

31. 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

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

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

34. Image Enhancement with averaging and median filtering

35. Image sharpening filters Edge detectors

36. What is edge? • Edges are the pixels where the brightness changes abrubtly. • It is a vector variable with magnitude and direction

37. EDGE PROFILES

38. 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 first derivativeGradient

39. 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 derivatives: Gradient

40. Continuous world second derivativeLaplacian • Δ2g(x,y) = ∂2g/ ∂2 x + ∂2 g/ ∂2 y

41. EDGES, GRADIENT AND LAPLACIAN

42. GRADİENT AND LAPLACIEN OF SMOOT EDGES, NOISY EDGES

43. GRADIENT EDGE MASKS Approximation in discrete grid

44. GRADIENT EDGE MASKS

45. Edge detection

46. Edge detection

47. Edge detection

48. LAPLACIAN MASKS

49. LAPLACIAN of GAUSSIAN EDGE MASKS

50. EDGE DETECTION