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

2. Background
Spatial domain refers to the aggregated of pixels composing an image.
Spatial domain methods are procedures that operate directly on these pixels.
Spatial domain processes will be denoted by the expression
g(x,y)=T [ f(x,y) ]
where f(x,y) is the input image, g(x,y) is the processed image,
and T is an operator of f

3. Some Basic Gray Level Transformations
Discussing gray-level transformation functions. The value of pixels, before and after processing are related by an expression of the form
s = T(r)
Where r is values of pixel before process
s is values of pixel after process
T is a transformation that maps a pixel
value r into a pixel value s.

4. Gray Level Transformations
Image Negatives
Log Transformations
Exponential Transformations
Power-Law Transformations

5. Image Negatives
The negative of an image with gray L levels is given by the expression
s = L – 1 – r
Where r is value of input pixel, and
s is value of processed pixel

7. Log Transformations
The general form of the log transformation shown in
s = c log(1+r)
Where c is constant, and it is assumed that r0

8. C = 1.0
C = 0.8

9. Exponential Transformations
The general form of the log transformation shown in
s = c exp(r)
Where c is constant, and it is assumed that r0

10. C = 1.0
C = 0.8

11. Power-Law Transformations
Power-law transformations have the basic form
Sometime above Equation is written as
Where c and  are positive constant.

12.  = 0.5
 = 1.0
 = 5.0

13. Piecewise-Linear Transformation Functions
Translate Mapping
Contrast stretching
Gray-level slicing
Bit-plane slicing

14. Translate Mapping

15. Contrast stretching
One of the simplest piecewise linear functions is a contrast-stretching transformation.
The idea behind contrast stretching is to increase the dynamic range of the gray levels in the image being processed.

16. Contrast stretching

17. Contrast stretching

18. Gray-level slicing
Highlighting a specific range of gray levels in an image often is desired.
There are several ways of doing level slicing, but most of them are variations of two basic themes.
One approach is to display a high value for all gray levels in the range of interest and a low value for all other gray levels.

19. Gray-level slicing
255
255

20. Bit-plane slicing
Instead of highlighting gray-level ranges, highlighting the contribution made to total image appearance by specific bits might be desired.
Suppose that each pixel in an image is represented b y 8 bits. Imagine that the image is composed of eight 1-bit planes, ranging from bit-plane 0, the least significant bit to bit-plane 7, the most significant bit.

21. Bit-plane 0
(least significant) 1 1 1 1 1
Bit-plane 7
(most significant)

22. Bit-plane slicing Original Image Bit-plane 7 Bit-plane 6 Bit-plane 4

23. Histogram Processing
The histogram of a digital image with gray levels in the range [0,L-1] is a discrete function
Where rk is the kth gray level and
nk is the number of pixels in the image having
gray level rk

24. HISTOGRAM
pixels
130 36 36 22 1 2 3
level
Image 16x14 = 224 pixels

25. Role of Histogram Processing
The role of histogram processing in image enhancement example
Dark image
Light image
Low contrast
High contrast

26. Histogram Processing Histogram Equalization
Histogram Matching(Specification)
Local Enhancement

27. Initial part of discussion
Let r represent the gray levels of the image to be enhanced.
Normally, we have used pixel values in the interval [0,L-1], but now we assume that r has been normalized to the interval [0,1], with r=0 representing black and r=1 representing white.

28. Histogram Equalization
We focus attention on transformations of the form
That produce a level s for every pixel value r in the original image.
And we assume that the transformation function T(r) satisfies the following conditions:
(a) T(r) is single-valued and monotonically increasing in the interval 0 ≤ r ≤ 1; and
(b) 0 ≤ T(r) ≤ 1 for 0 ≤ r ≤ 1

29. Reason of Condition
The requirement in (a) that T9r) be single valued is needed to guarantee that the inverse transformation will exist, and the monotonicity condition preserves the increasing order from black to white in the output image.
Condition (b) guarantees that the output gray levels will be in the same range as the input levels.

30. Transformation function
Level s
sk=T(rk)
T(r)
Level r rk 1

31. Note The inverse transformation from s back to r is denoted
There are some cases that even if T(r) satisfies conditions (a) and (b), it is possible that the corresponding inverse
T-1(s) may fail to be single valued.

32. Fundamental of random variable
PDF (probability density function) is the probability of each element
CDF (cumulative distribution function) is summation of the probability of the element that value less than or equal this element

33. PDF The PDF (probability density function) is denoted by p(x)
ตัวอย่าง การทอยลูกเต๋า ความน่าจะเป็นที่ลูกเต๋าจะขึ้นหน้าต่างๆ เป็นดังต่อไปนี้
pdf
1/6
หน้าลูกเต๋า 1 2 3 4 5 6

34. CDF The CDF (cumulative density function) is denoted by P(x)
ตัวอย่าง การทอยลูกเต๋า ความน่าจะเป็นที่ลูกเต๋าจะขึ้นหน้าต่างๆ เป็นดังต่อไปนี้
cdf 1
1/6
หน้าลูกเต๋า 1 2 3 4 5 6

35. Relation between PDF and CDF
PDF can find from this equation
CDF can find from this equation

36. Idea of Histogram Equalization
The gray levels in an image may be viewed as random variables in the interval [0,1].
Let pr(r) denote the pdf of random variable r and
ps(s) denote the pdf of random variable s; if pr(r) and T(r) are known and T-1(s) satisfies condition (a), the formula should be

37. Idea of Histogram Equalization
By Leibniz’s rule that the derivative of a definite integral with respect to its upper limit is simply the integrand evaluated at that limit.

38. Idea of Histogram Equalization
Substituting into the first equaltion

39. Idea of Histogram Equalization
The probability of occurrence of gray level rk in an image is approximated by

40. Histogram Equalization

41. Example for Histogram Equalization
จงทำฮิสโตแกรม อีควอไรเซชั่น ของภาพที่มีรายละเอียดต่อไปนี้
ค่าสี
จำนวนพิกเซล
(nj) 30 1 50 2
100 3
1500 4
2300 5
4000 6
200 7 20
สูตร

42. Example for Histogram Equalization

43. Example for Histogram Equalization

44. Example for Histogram Equalization(2)
ค่าสี
(k)
จำนวนพิกเซล(nj) 30
0.0037
0.0256 1 50 80
0.0098
0.0683 2
100
180
0.0220
0.1537 3
1500
1680
0.2050
1.4241 4
2300
3980
0.4854
3.3976 5
4000
7980
0.9732
6.8122 6
200
8180
0.9976
6.9830 7 20
8200
1.0000
7.0000

45. Histogram Specification
can called “Histogram Matching”
Histogram equalization automatically determines a transformation function that seeks to produce an output image that has a uniform histogram.
In particular, it is useful sometimes to be able to specify the shape of the histogram that we wish the processed image to have.

46. Meaning: Histogram Specification
In this notation, r and z denote the gray levels of the input and output (processed) images, respectively.
We can estimate pr(r) from the given image
While pz(z) is the specified probability density function that we wish the output image to have

47. Meaning: Histogram Specification
Let s be a random variable with the property
Suppose that we define a random variable z with property
Then two equations imply G(z)=T(r)

48. Procedure: Histogram Specification
Use Eq.(1) to obtain the transformation function T(r)
Use Eq.(2) to obtain the transformation function G(z)
Obtain the inverse transformation function G-1
Obtain the output image by applying Eq.(3) to all the pixels in the input image

49. Input Image
Specified histogram

50. Result form Histogram Specification

51. Local Enhancement
Normally, Transformation function based on the content of an entire image.
Some cases it is necessary to enhance details over small areas in an image.
The histogram processing techniques are easily adaptable to local enhancement.

52. Local Enhancement
Pixel-to-pixel translation
Nonoverlapping region

53. Local Equalization

54. Enhancement Using Arithmetic/Logic Operations
Are performed on a pixel-by-pixel basis between two or more images
Logic operations are concerned with the ability to implement the AND, OR, and NOT logic operators because these three operators are functionally complete.
Arithmetic operations are concerned about +,-,*, / and so on (arithmetic operators)

55. Image Subtraction
The difference between two images f(x,y) and h(x,y) expressed as
g(x,y) = f(x,y) – h(x,y)

57. Image Averaging
Consider a noisy image g(x,y) formed by the addition of noise to original image f(x,y)
The objective of this procedure is to reduce the noise content.
Where the assumption is that at every pair of coordinates(x,y) the noise is uncorrelated and has zero average value.

58. Image Averaging Let there are K different noisy images
If an image is formed by averaging K different noisy images

59. Image Averaging (Gray Scale)2
images 5
images 10
images 20
images

60. Image Averaging (Color Image)
(1)
(2)
(3)
Average image