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İmage enhancement

İmage enhancement. Prepare image for further processing steps for specific applications. Image enhancement: Pre-processing. Spatial domain techniques: Find a transformation T f(x,y) g(x,y) Frequency domain techniques

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İmage enhancement

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  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

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