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) F -1 F T 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 L-1 r s S=r

5. Gray Scale Transform q=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

12. Image Enhancement by Gray Scale Transform

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

17. Chapter 3 Image Enhancement in the Spatial Domain Chapter 3 Image Enhancement in the Spatial Domain

18. Histogram processing Given an image with L gray levels h(r k ) = n k r k : kth gray level n k : number of pixels with gray level r k 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 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 Processing Convolution 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 Image Enhancement WITH AVERAGING AND THRESHOLDING

31. Restricted Averaging Apply averaging to only pixels with brightness value outside a predefined interval. Mask h(i,j) = 1For 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 021 212 332 021 222 332 0 1 1 2 2 2 2 3 3

33. Median filtering breaks the straight lines 55555 55555 00000 55555 55555 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. Continuous world first derivative 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

39. Discrete world derivatives: Gradient 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) |

40. Continuous world second derivative Laplacian Δ 2 g(x,y) = ∂ 2 g/ ∂ 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 GRADIENT EDGE MASKS Approximation in discrete grid

44. GRADIENT EDGE MASKS

45. Edge detection

48. LAPLACIAN MASKS

49. LAPLACIAN of GAUSSIAN EDGE MASKS

50. EDGE DETECTION

53. HOUGH TRANSFORM

54. PARAMETER PLANE OF HOUGH TRANSFORM

55. HOUGH TRANSFORM IN POLAR FORM

56. HOUGH TRANSFORM OF POINTS IN POLAR FORM

57. Chapter 10 Image Segmentation Chapter 10 Image Segmentation

58. Chapter 10 Image Segmentation Chapter 10 Image Segmentation

59. GRADIENT OPERATIONS

60. Image Enhancement WITH LAPLACIAN AND SOBEL

61. Image Enhancement (cont.)

62. Edg Detection with Laplacian

63. Image Enhancement with high pass filter

64. Edge Detection with High Boost

65. Laplacian Operator

66. Image Enhancement with Laplacian

67. Chapter 3 Image Enhancement in the Spatial Domain Chapter 3 Image Enhancement in the Spatial Domain

68. Chapter 3 Image Enhancement in the Spatial Domain Chapter 3 Image Enhancement in the Spatial Domain

69. Chapter 3 Image Enhancement in the Spatial Domain Chapter 3 Image Enhancement in the Spatial Domain

70. Histogram Equalization

71. Chapter 3 Image Enhancement in the Spatial Domain Chapter 3 Image Enhancement in the Spatial Domain

72. Chapter 3 Image Enhancement in the Spatial Domain Chapter 3 Image Enhancement in the Spatial Domain

73. Chapter 3 Image Enhancement in the Spatial Domain Chapter 3 Image Enhancement in the Spatial Domain

74. Chapter 3 Image Enhancement in the Spatial Domain Chapter 3 Image Enhancement in the Spatial Domain

75. Chapter 3 Image Enhancement in the Spatial Domain Chapter 3 Image Enhancement in the Spatial Domain

76. Chapter 3 Image Enhancement in the Spatial Domain Chapter 3 Image Enhancement in the Spatial Domain

77. Chapter 3 Image Enhancement in the Spatial Domain Chapter 3 Image Enhancement in the Spatial Domain

78. Chapter 3 Image Enhancement in the Spatial Domain Chapter 3 Image Enhancement in the Spatial Domain

79. Chapter 3 Image Enhancement in the Spatial Domain Chapter 3 Image Enhancement in the Spatial Domain