Image enhancement techniques can be divided into spatial and frequency domain methods. Spatial domain methods operate directly on pixel values using techniques like basic gray level transformations, contrast stretching and thresholding. These manipulations are used to accentuate image features, improve display quality or aid machine analysis by modifying pixel intensities within an image.

1. IMAGE ENHANCEMENT

2. Image Enhancement - Objectives
 To process an image so that the result is more suitable than the
original image for a specific application.
 Accentuation or sharpening of image features such as edges,
boundaries, or contrast to make an image more useful for
display and analysis.
 When images are processed to improve their appearance to
human viewers, the objective may be to improve perceptual
aspects, such as image quality, intelligibility, or visual
appearance. In other applications, such as object identification
by a machine, an image may be preprocessed to aid machine
performance.

3. Types Of Processing
 The processing techniques are very much problem
oriented. Means an image enhancement algorithm
that performs well for one class of images may not
perform well for other classes.
 There are several enhancement techniques available,
and we can classify them into two broad categories.
 Spatial domain methods – Direct manipulations of pixels
in an image.
 Frequency domain methods – Modifying the fourier
transform of an image.

4. Spatial domain Methods
 The spatial domain refers to the image plane itself, that is
the collection of pixels constituting an image.
 The spatial domain process can be denoted as
 f(x,y) - input image; g(x,y) - processed image;
T – operator on f, defined over some neighbourhood of
f(x,y)
 The neighbourhood about point (x,y) is a square or
rectangular sub image area centered at (x,y).
[ ]),(),( yxfTyxg =

5. Spatial domain Methods
 The simplest form of T is when the neighbourhood is of size
1x1, that is a single pixel.
 Then g depends only on the value of f, at (x,y) or the gray
level f(x,y), and T becomes a gray level transformation
function of the form
 s = T(r). s – gray level of g(x,y)
 r – gray level of f(x,y) at any point (x,y)
 This type of processing is known as point processing,
because the processing depends only on the gray level at that
point.

6.  When T operates on the neighbourhood of f(x,y), the processing is
done by the use of masks (say a 3x3 2-D array) and is known
as mask processing. The mask coefficients determine the nature of
process.
Spatial domain Methods

7. Basic Gray level Transformations
 Three basic types of functions used for gray
level transformations in image enhancement
are
 Linear.
 Logarithmic.
 Power low.

9. Linear Transformations
 Identity Transformation:

10. Linear Transformations Cont..
 Negative Transformation:
The negative of a digital image with gray levels in the range
[0,L-1] is obtained by the transformation function
 s = T(r) = L-1-r = 255-r, for an 8-bit image
r
s

11. Digital Negative
 Digital negatives are useful in the display of medical images and their
processing

12. Logarithmic Transformations
 The general form of a log transformation is given by
 s = c log (1+r)
 The transformation maps a narrow range of low gray level
values in the input image to a wider range of output levels.
The higher gray level values are compressed to a narrow
range.
 Useful for enhancing details in the darker regions of the image
at the expense of detail in the brighter regions
 If the dynamic range of an image data is very large (eg.
Dynamic range of a transformed image), only a few pixels will
be visible. The transformation compresses the dynamic range.

14.  Exponential
 The effect is the reverse of that obtained with logarithmic
mapping.

15. Exponential Contrast Enhancement -
Example

16. Power Law Transformations
 The power law transformations have the basic form given by
 Power law curves with fractional values of γ map a narrow
range of dark input values into a wider range of output
values, with the opposite being true for higher values of
input levels.
 A family of transformation curves are possible for different
values of γ. The curves generated with values of γ>1 have
the opposite effect as those generated with values of γ<1.
γ
crs =

18. Power Law Transformation - Application
 A variety of devices used for image capture, printing
and display respond according to power law.
 The exponent in the power law equation is referred
to as gamma and the process used to correct this
power law response phenomenon is gamma
correction.
 CRT devices have a intensity to voltage response
that is a power function with gamma varying from
1.8 to 2.5.

19. Gamma correction - Example

22. Contrast Stretching
 Low contrast images can result from poor illumination, lack
of dynamic range in the imaging sensor or wrong setting of a
lens aperture during image acquisition.

23. Contrast Stretching Cont..
 A typical contrast stretching transformation is given, which
can be expressed as
 The slope of the transformation is chosen greater than unity
in the region of stretch.
 The idea is to increase the dynamic range of the gray levels
in the image being processed.
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=
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24. αi > 1 - Range Stretching
αi< 1 - Range Compression

25. Contrast Stretching Cont..
 Let T(a1) = s1 and T(a2) = s2
 If a1 = s1 and a2 = s2 – linear transformation with no
changes in the gray levels.
 If a1=a2 and s1 = 0; s2 = L-1 – thresholding function
which creates a binary image.
 Any intermediate values of (a1,s1) and (a2,s2)
produce various degrees of spreads in the gray
levels of the output image, which affects its
contrast.

26. Thresholding Function

27. Thresholding - Example

30. Gray Level Slicing
 Highlighting a specific range of gray levels
in an image.

31. Result of Curve 1

32. Result of Curve with linear variation
from b to L

33. Result of Curve 2

34. Result of Curve 3

35. Result of Curve 4

36. Thresholding Example
a)Original image; b)Thresholded at 118

37. Bit Plane Slicing
 Highlighting the contributions made to total
image appearance by specific bits.
 An 8-bit image contains 8 bit planes.
 We can analyze the relative importance of
each bit plane an image.

38. Bit Plane Slicing - Example