1. Image Enhancement

2. References
Gonzalez and Woods, “ Digital Image Processing,” 2nd Edition, Prentice Hall, 2002.
Jahne, “Digital Image Processing,” 5th Edition, Springer 2002.
Jain, “Fundamentals of Digital Image Processing,” Prentice Hall 1989
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3. Overview Human perception (focus of this discussion)
Machine perception (ocr)
Application specific
Heuristic based: result better than the original image – subjective assessment
Spatial vs frequency domain
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4. Spatial Domain Based on the collection of pixels in the image
Enhancement techniques will yield
Noise reduction
Neighborhood smoothing
Highlighting of desired feature
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5. Spatial Domain – Math Framework
From [1]
Typically, spatial domain based enhancement involves:
g(x,y) = T [f(x,y)],
where f = input image; g = output image; T = operator defined on f based on a neighborhood of x,y. If the neighborhood is 1x1 pixel, then the output intensity is dependent on the current intensity value of the pixel, and can be represented as
s = T( r ) where r and s are gray level values of f(x,y) and g(x,y) at location x,y. In such situations T is a gray-level transformation function.
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6. Examples of Gray level Transformation Functions (Point Processing)
From [1]
Contrast Stretching
Best if input is 0 outside a range of values.
Thresholding: Result is a binary Image
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7. Larger Neighborhoods
Objective – determine g(x,y) based on input intensity (gray level) values f(x,y) in the neighborhood of x,y.
Mask processing or filtering
Each of the elements in the neighborhood has an associated weight
g(x,y) depends on f(a,b)|a,b є N(x,y)
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8. Basic Gray Level Transformations
Light
From [1]
Dark
Dark
Light
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9. Image Negatives From [1]
In this example, using the image negative, it is easier to analyze the breast tissue.
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10. Log transformations s = c log (1 + r)
r >= 0; hence 1 + r > 0; log 0 = ?
Log transformations are useful, when there is a large dynamic range for the input variable ( r ).
From [1]
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Range: 0 to 1.5*106
Range: 0 to 6.2

11. Power law transformation
From [1]
Stretch higher (lighter) gray levels
Many display devices (e.g. CRT) respond like the power law, i.e intensity – voltage relationship is power law based gamma 1.8 to The display will tend to produce images darker than intended. So the display is distorted. Gamma correction is used to correct for this distortion.
Stretch lower (ldarker) gray levels
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12. Display distortion correction
Gamma correction can also fix the distortions in color.
More important with the internet.
Many viewers, variety of monitors.
Gamma of view station is not known.
Preprocess using an average gamma.
From [1]
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13. Power law – contrast manipulation
(c) better than (b).
(d) Background is better than (c) but washed out effect.
From [1]
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14. Piece-wise Linear Transformation Contrast Stretching
From [1]
Mean gray level value of the image
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15. Gray level Slicing
From [1]
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16. Bit plane Slicing
MSb (bit 7)
From [1]
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LSb (bit 0)

17. Histograms Histogram - frequency of occurrence of a gray level value
Normalizing histograms with rest to the total number of pixels converts these into probability density like function
Histogram processing yields robust image processing results
Histograms are NOT unique
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18. Histograms for 4 images From [1]
For high contrast, it is best to have a larger range of gray level values.
If we could transform an image with a resulting change in histogram, then that may yield more contrast.
We need to study the rules for transforming histograms, and study the resulting impact on images.
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From [1]

19. Transformation From [1] s s1 r r s1 s
Applying transformations to histograms, can use results from probability.
Consider the transformation S = T( r ); 0 <= r <=1 which has the following properties:
T( r ) is single-valued and monotonically increasing in [0,1]
0 <= T( r ) <= 1 for r in [0,1]
Single valued condition ensures that an inverse transformation exists, and the monotonic condition ensures the increasing order of the pixels from black to white.
(a) and (b) do not ensure that the inverse transform is single valued. s1 r r s1 s
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20. Histogram Equalization
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From [1]

21. Transformation Functions
From [1]
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22. Mapping for Histogram Specification
From [1]
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23. Example of Histogram Specification
From [1]
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24. Continued
From [1]
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25. Localized Histogram Equalization
From [1]
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26. From [1]
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27. Histogram Stats
From [1]
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