1. Image Enhancement
Prepared by
Aya Elshiwi
Supervisor
Dr.Osama Ouda

2. outline
• Introduction
• Image enhancement methods:
Spatial-Frequency domain enhancement methods
Point operations
Histogram operations
Spatial operations
Transform operations
• Multi-spectral image enhancement
• False color and pseudocoloring
• Color image enhancement

3. Introduction
• The principal objective of image enhancement is to process a given
image so that the result is more suitable than the original image
for a specific application.
• It accentuates or sharpens image features such as edges,
boundaries, or contrast to make a graphic display more helpful for
display and analysis.
• The enhancement doesn't increase the inherent information
content of the data, but it increases the dynamic range of the
chosen features so that they can be detected easily.

4. Cont.
• The greatest difficulty in image enhancement is quantifying the
criterion for enhancement and, therefore, a large number of
image enhancement techniques are empirical and require
interactive procedures to obtain satisfactory results.
• Image enhancement methods can be based on either spatial or
frequency domain techniques.

5. Spatial-Frequency domain enhancement methods
Spatial domain enhancement methods:
• Spatial domain techniques are performed to the image plane itself and
they are based on direct manipulation of pixels in an image.
• The operation can be formulated as g(x,y) = T[f(x,y)],
where g is the output, f is the input image and T is an operation on f
defined over some neighborhood of (x,y).
• According to the operations on the image pixels, it can be further
divided into 2 categories: Point operations and spatial operations.
Frequency domain enhancement methods:
• These methods enhance an image f(x,y) by convoluting the image with a
linear, position invariant operator.
• The 2D convolution is performed in frequency domain with DFT.
Spatial domain: g(x,y)=f(x,y)*h(x,y)
Frequency domain: G(w1,w2)=F(w1,w2)H(w1,w2)

6. Point operations
-Zero-memory operations where a given gray level u∈[0,L] is mapped
into a gray level v∈[0,L] according to a transformation.
v(m,n)=f(u(m,n))

7. 1-contrast stretching
• The idea behind contrast stretching is to increase the dynamic
range of the gray levels in the image being processed.
• Low contrast images occur often due to :
-poor or nonuniform lightning conditions
-small dynamic range of imaging sensors
• Expressed as :
-For dark region stretch α>1 ,a=L/3
-For mid region stretch β>1 ,b=2/3L
-For bright region stretch γ>1

8. Example 1
• (b) a low-contrast image : results
from poor illumination, lack of
dynamic range in the imaging
sensor, or even wrong setting of
a lens aperture of image
acquisition
• (c) result of contrast stretching :
(r1,s1) = (rmin,0) and (r2,s2) =
(rmax,L-1)
• (d)result of thresholding

9. Example2

10. 2-Clipping and Thresholding
• Expressed as :
• Clipping:
-Special case of contrast stretching ,where α= γ=0
-Useful for noise reduction when the input signal is known to lie in the
range [a,b].

11. Cont.
• Thresholding:
- is a special case of case of clipping where a=b=t and the output
comes binary.
Example 1
Clipping and Thresholding

12. Example2

13. 3-Digital negative
• Negative image can be obtained by reverse scaling of the gray levels
according to the transformation,
v=L-u
• Useful in the display of medical images.
• Example:

14. 4-intensity level slicing
• Permit segmentation of certain gray level regions from the rest of the
image.

15. 5-Bit extraction
• This transformation is useful
In determining the number of
Visually significant bits in an
Image.
• Suppose each pixel is
represented by 8 bits it is desired
To extract the nth most significant bit
And display it .
• Higher-order bits contain the
majority of the visually significant data

16. Example
8-bit fractal image
• The (binary) image for bit-plane 7 can be obtained by processing the
input image with a thresholding gray-level transformation.
-Map all levels between 0 and 127 to 0
-Map all levels between 129 and 255 to 255

17. Cont.
8-bit plane image

18. 6-Range compression
• Sometimes the dynamic range of a processed image far exceeds the
capability of the display device, in which case only the brightest parts
of the images are visible on the display screen.
• An effective way to compress the dynamic range of pixel values is to
perform the following intensity transformation function:
s = c log(1+|u|)
where c is a scaling constant, and the logarithm function performs the
desired compression.

19. 7-Image subtraction and change detection
• In many imaging applications it is desired to compare two
complicated or busy images .
• A simple ,but powerful method is to align the two images and
subtract them .The difference image is then enhanced .
• Applications such as imaging of the blood vessils and arteries in a
body , security monitoring systems .
• Example:
_

20. Histogram modeling
Histogram modeling techniques modify an image so that it’s histogram has a
desired shape . This is useful in stretching the low contrast levels with
narrow histograms .
It is possible to develop a transformation function that can automatically
achieve this effect ,based on histogram of input image .

21. Histogram modeling Cont.
Image Histogram

22. 1-Histogram equalization
• The objective is to map an input image to an output image such that
its histogram is uniform after the mapping.
• Let r represent the gray levels in the image to be enhanced and s is
the enhanced output with a transformation of the form s=T(r).
• Assumptions
• Possible for multiple values of r to map to a single value of s.

23. Histogram Equalization cont.
Example 1:

24. Solution

25. Solution cont.

26. Solution cont.

27. Example 2: Equalizing an image of 6 gray levels

29. Histogram specification
• Histogram equalization only generates an approximation to a uniform
histogram.
• With Histogram Specification, we can specify the shape of the
histogram that we wish the output image to have.
• It doesn’t have to be a uniform histogram.
• The principal difficulty in applying the histogram specification
method to image enhancement lies in being able to construct a
meaningful histogram.

30. Histogram specification cont.

31. Histogram specification cont.
Procedure :

32. Example

33. Example cont.

35. Spatial operations
• Operations performed on local neighborhoods of input pixels
• Image is convolved with [FIR] finite impulse response filter called
spatial mask .
• Techniques such as :
- Noise smoothing
- Median filtering
- LP,HP &PB filtering
- Zooming

36. Spatial averaging and spatial low-pass filtering
•
Each pixel is replaced by a weighted average of it’s neighborhood pixels
that is,
-y(m,n) and v(m,n) are the input and output images ,respectively.
-W is suitably chosen window .
-a(k,l) are the filter weights .
• A common class of spatial averaging filters has all equal weights,
• Used for Noise smoothing , low-pass filtering and subsampling of images.

37. • Examples of spatial averaging masks
• Spatial averaging is used for Noise smoothing

38. Example

39. directional smoothing
• To protect edges from blurring while smoothing.
•
spatial averages are calculated in several directions , and the direction
giving the smallest changes before and after filtering is selected.
• The direction (θ) is found such that
• Then
gives the desired result.
is minimum

40. Median filtering
• Input pixel is replaced by the median of the pixels contained in a window
around a pixel
• The algorithm requires arranging the pixels in an increasing or decreasing
order and picking the middle value.
• For Odd window size is commonly used [3*3-5*5-7*7]
• For even window size the average of two middle values is taken.
• Median filter properties:
1- Non-linear filter
2-Performes very well on images containing binary noise , poorly when the
noise is gaussian.
3-performance is poor in case that the number of noise pixels in the window is
greater than or half the number of pixels in the window.

41. Example:

42. Example 2:

43. Unsharp masking and crispening
• The unsharp masking techniques is used commonly in printing industry for
crispening the edges.
• It is applied by subtracting an unsharp or smoothed or low-pass filtered version of
an image from the original image.
• It is equivalent to adding the gradient, or high-pass signal to the image as shown in
figure.
Unsharp masking operations

44. Unsharp masking and crispening cont.
• Unsharp masking operation can be represented by :
v(m,n) = u(m,n) + λg(m,n)
Where λ > 0 and g(m,n) is a suitably defined gradient at (m,n).
• A commonly used gradient function is the discrete laplacian.

45. Example : unsharp masking using laplacian operator

46. Spatial low-pass ,high-pass and band-pass Filtering
• Spatial averaging operations is a low-pass filter.
• High-pass filter can be implemented by subtracting low-pass filter output
from it’s input.
• Band-pass filter can be characterized as:
where hl1 ,hl2 represent short and long term averages.

47. • Low-pass filters are useful for noise smoothing and interpolation .
• High-pass Filters are useful in extracting edges and in sharpening images.
• Band-pass filters are useful in the enhancement of edges and other highpass image characteristics in the presence of noise .
• Example1

48. • Example2

49. Inverse contrast mapping and statistical scaling
• The ability of our visual system to detect an object in a uniform background
depends on it’s size and the contrast ratio which is defined as
γ
= σ/ μ
• where μ is the average luminance of object σ is the standard deviation of the
luminance of the object plus it’s surround.
• Now consider the inverse contrast ratio transformation
Where μ(m,n) and σ(m,n) are the local mean and standard deviation of u(m,n)
measured over a window W and are given by:
• This transformation generates an image ,where the weak(low-contrast) edges are
enhanced.

50. • A special case of this transformation
•
which scales each pixel by it’s standard deviation to generate an image
whose pixels have unity variance .
• This mapping is also called statistical scaling.
• Example:

51. Magnification and interpolation (zooming)
• Often it is desired to zoom on a given region of an image .This requires
taking an image and displaying it as a larger image .
• Techniques : -Replication
-linear interpolation

52. Zooming by Replication

53. • Example

54. Zooming by interpolation

55. • Example

57. Transform operations
• In the transform operations enhancement techniques , zero memory
operations are performed on a transformed image followed by the inverse
transformation.
• We start with the transformed image V={v(k,l)} as
V = AUAT
Where U = {u(m,n)} is the input image .
• Then the inverse transform of V’(k,l)=f(v(k,l)) gives the enhanced image as

58. Generalized linear Filtering
• The zero memory transform domain operation is a pixel by pixel
multiplication .
Where g(k,l) is called a zonal mask.

60. • A filter of special interest is the inverse gaussian filter ,whose zonal mask for
N.N images is defined as:
When A is a DFT.
• For other orthogonal transforms the gaussian zonal mask is used as :
• Usually, the inverse Gaussian filter is used as high-frequency filter that
restore the blurred images.
Example:
Inverse gaussian Filtering

61. Root Filtering
• The transform coefficients v(k,l) can be written as:
• In root filtering, the α-root of the magnitude component of v(k,l) is taken,
while retaining the phase component, to yield
• For common images, since the magnitude of v(k,l) is relatively smaller at
higher frequencies, the effect of α -rooting is to enhance higher frequencies
(low amplitudes) relative to lower frequencies (high amplitudes).
• The following figure shows the effect of these filters

62. Generalized cepstrum and homomorphic filtering
• If the magnitude term of the root filtering equation is replaced by the
logarithm of |v(k,l)| such as:
• Then the inverse transform of s(k,l) denoted by c(m,n) is called generalized
cepstrum of the image.
• The image c(m,n) is also called the generalized homomorphic transform of
the image u(m,n).
• Inverse homomorphic transform

63. • The generalized homomorphic linear filter performs zero-memory
operations on H -transform of the image followed by inverse H transform
• Example:
cepstrum of the building image
(a) original image (b) DFT
(c) DCT
(d) Hadamard transform
• The homomorphic transformation reduces the dynamic range of the image
in the transform domain and increase it in the cepstral domain.

64. Multispectral image enhancement
• In multispectral imaging there is a sequence of I images Ui(m,n) ,i=0,1,2….L
where L is typically between 2 and 12.
• It is desired to combine these images to generate a single or a few display
images that are representative of their features.
• Three methods to enhance such images:
-Intensity ratios
-Log ratios
-Principal components

65. Intensity ratios
•
Define the ratios:
• Where ui(m,n) represents the intensity and is assumed to be positive.
• This methods gives I2-I combinations for the ratios the most suitable of
which are chosen by visual inspection.
• Sometimes the ratios are defined with respect to the average image
to reduce the number of combinations.

66. Log ratios
• Taking logarithm of both sides
• The log ratio Li,j gives a better display when the dynamic range of Ri,j is very
large which can occur if the spectral features at a spatial location are quite
different.

67. Principal components
• For each (m,n) define I*1 vector
• The I*I KL transform of u(m,n) denoted by (Φ) is determined from auto correlation matrix of the ensemble of vectors {ui(m,n),i=0…I}.
• The rows of (Φ)which are eigen vectors of the auto correlation matrix are
arranged in decreasing order of their associated eigen values .
• Then for any I0<I,the images vi(m,n),i=0….I0 obtained from the KL
transformed vector.

68. False color and pseudocolor
-Human can distinguish more colors than gray levels.
-False color: mapping a color image into another color image to provide a more
striking color contrast e.g to attract attention of human.
-pseudocoloring : mapping a set of images into a color image .
usually different features represented by different color.
Pseudocolor image enhancement

69. • Other methods are possible ,including a pseudorandom mapping of gray
levels into R,G,B coordinates , as is done in some image display systems.
• For image data set where the number of images is greater than or equal to
three , the data set can be reduced to three ratios, three log-ratios or three
principle components , which are then mapped into suitable colors.
•
In general, the pseudocolor mappings are nonunique , and extensive
interactive trials may be required to determine an acceptable mapping for
displaying a given set of data.

70. Color image enhancement
• Color image enhancement may require improvement in color balance or
color contrast in a color image.
• To enhance color images :
- The input color coordinates of each pixel are independently transformed
into another set of color coordinates.
- Apply enhancement algorithm for individual monochrome images.
• Since each image plane Tk (m,n),k=1,2,3 is enhanced independently ,care
has to be taken so that the enhanced coordinates T’k are within the color
gamut of R-G-B system.

71. Questions ?