presentation on Spatial domain methods of image enhancement

1. Image Enhancement in
Spatial Domain
by
P. S. Patil

2. Image enhancement
 Image Enhancement process
 Image enhancement schemes:
Spatial domain enhancement methods
Frequency domain enhancement methods
Point operations Vs neighborhood operations
Histogram methods

3. Introduction
• The image enhancement is the process of making image
under consideration suitable for a specific application
• different methods of image enhancement:

4. Spatial Domain transformation
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( , ) [ ( , )])
( , ) :input image
( , ) :output image
:an operator on defined over
a neighborhood of point ( , )
g x y T f x y
f x y
g x y
T f
x y


5. Spatial domain enhancement methods
 Spatial domain techniques are performed in the image plane
 It is based on modifying the pixels in an image directly.
Types
1) Point operations.
2) Spatial operations.

6. Frequency Domain Enhancement
• In this method image is transformed into frequency
domain and transformed coefficients are manipulated.
Weeks 1 & 2 6

7. Basic Transformation Functions
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8. contrast stretching
• The idea behind contrast stretching is to increase the
dynamic range of the gray levels in the image
• Mathematical modeling :
-For α<1 ,a=L/3
-For β>1 ,b=2/3L

9. Thresholding:
- Thresholding is an extreme contrast stretching which
results into binary image

10. Digital negative
• Negative image can be obtained by,
• S= T(r)= L-r-1
Image negatives
1
s L r
  

11. Gray level slicing
• Certain areas from the image under consideration can by
highlighted using this transformation.

12. Log Transformations for image enhancement
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Log Transformations
log(1 )
s c r
 

13. The histogram
13
The histogram of an image shows us the
distribution of grey levels in the image

14. Image enhancement by histogram Processing
 Histogram Equalization
 Histogram specification
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15. Histogram Equalization
• Histogram equalization is a method in image
processing of contrast adjustment using the image's histogram.
• This method usually increases the global contrast of many
images, especially when the usable data of the image is
represented by close contrast values.
• Through this adjustment, the intensities can be better distributed
on the histogram.
• This allows for areas of lower local contrast to gain a higher
contrast.
• Histogram equalization accomplishes this by effectively spreading
out the most frequent intensity values.
Weeks 1 & 2 15

16. Weeks 1 & 2 16

17. Weeks 1 & 2 17

18. Neighbourhood Operations
Neighbourhood operations simply operate on a
larger neighbourhood of pixels than point
operations
Neighbourhoods are
mostly a rectangle
around a central pixel
Any size rectangle
and any shape filter
are possible
Origin x
y Image f (x, y)
(x, y)
Neighbourhood

19. Thank you
Weeks 1 & 2 19