Point processing

POINT PROCESSING & GRAY LEVEL
TRANSFORMATIONS
Presented By,
K. Annapushpam M.Phil (Computer Science)
P. Anupriya M.S.University
M. Chithra Tirunelveli
R. Debi Stella

CONTENTS
 Point processing
 Basic gray level transformations
 Basic gray level transformation graph
 Linear transformation
 Negative transformation
 Identity transformation
 Log transformation
 Power law transformation
 Piecewise linear transformation functions
 Contrast stretching
 Intensity level slicing
 Bit plane slicing
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Pointprocessing&Grayleveltransformations

POINT PROCESSING
 Image enhancement is the process of adjusting digital images
so that the results are more suitable for display or
further image analysis.
 It has two broad categories:
 Spatial domain methods
 Frequency domain methods
 Spatial domain methods are operate directly on the pixels.
 Point processing operation deals with pixel intensity values
individually.
3

POINT PROCESSING (CONTD)
 The intensity values are altered using particular
transformation techniques as per the requirement.
 Enhanced at any point in an image depends only on the gray
level at that point techniques are referred as point processing.
 Most spatial domain enhancement operations can be reduced
to the form of,
g (x, y) = T[ f (x, y)]
 In this case T is referred to as a gray level transformation
function or a point processing operation. 4

 where f (x, y) is the input image, g (x, y) is the processed
image and T is point operator defined over some
neighborhood of (x, y).
 Point processing operations take the form of,
s = T ( r )
 where s refers to the processed image pixel value and r refers
to the original image pixel value.
 Mask is a small matrix useful for blurring, sharpening, edge
detection.
5

 New image is generated by multiplying the input image with
the mask matrix. Mask can be in any dimension (i.e 3x3, 4x4).
 Contrast stretching expands the range of intensity levels in an
image.
 Extreme contrast stretching yields Thresholding.
 Thresholding image has maximum contrast as it has only
Black & White gray values.
 Brightness enhancement is shifting of intensity values to
higher level.
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BASIC GRAY LEVEL TRANSFORMATION
 There are three basic gray level transformation.
 Linear
 Logarithmic
 Power – law
7

GRAY LEVEL TRANSFORMATION GRAPH
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LINEAR TRANSFORMATION
 Linear transformation includes following two categories,
 Negative Transformation
 Identity Transformation
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NEGATIVE TRANSFORMATION
 Negative images are useful for enhancing white or gray
detail embedded in dark regions of an image.
 Negative transform exchanges dark values for light values
and vice versa.
 The Negative Transformations can be defined by,
s =( L-1-r)
 Negative of an image intensity levels in the range [0,L-1],
L-1 = Maximum pixels value
r = Pixel value of an image 10

NEGATIVE TRANSFORMATION EXAMPLE
Graph representation
Input image Output image
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IDENTITY TRANSFORMATION
 Each value of the input image is directly mapped to each
other value of output image.
 That results in the same input image and output image.
Graph representation
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LOG TRANSFORMATION
 This transform is used to expand values of dark pixels
and compress values of bright pixels.
 It maps a narrow range of low level gray scale intensities
into wider range of output values.
 Similarly maps the wide range of high level gray scale
intensities into a narrow range of high level output
values.
 The log transformations can be defined by this formula
s = c log(r + 1)
13

LOG TRANSFORMATION (CONTD)
 Where s and r are the pixel values of the output and the
input image and c is a constant.
 The value 1 is added to each of the pixel value of the
input image because if there is a pixel intensity of 0 in
the image, then log (0) is equal to infinity.
 So 1 is added, to make the minimum value at least 1.
 The inverse log transform is opposite to log transform.
14

LOG TRANSFORMATION EXAMPLE
InvLog Log
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POWER LAW TRANSFORMATION
 This type of transformation is used for enhancing images
for different type of display devices.
 These transformations can be given by,
s=cr^γ
 Here, s is output pixel value, r is the input pixel value, c
and γ are real numbers.
 Variation in the value of γ varies the enhancement of the
images. This technique is commonly called as Gamma
correction. 16

POWER LAW TRANSFORMATION
 Different display monitors display images at different
intensities and clarity because every monitor has built in
gamma correction in it with certain gamma ranges.
 A good monitor automatically corrects all the images
displayed on it for the best contrast to give user the best
experience.
 The difference between the log transformation function
and the power law functions is that using the power law
function a group of possible transformation curves can
be obtained just by varying γ.
17

POWER LAW TRANSFORMATION (CONTD)
Various values for γ
18

POWER LAW TRANSFORMATION EXAMPLE
Gamma=10 Gamma=8 Gamma=6
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PIECEWISE LINEAR TRANSFORMATION FUNCTION
 There are three basic piecewise linear transformation
functions.
 Contrast stretching
 Intensity level slicing
 Bit plane slicing
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CONTRAST STRETCHING
 It enhance the low contrast images.
 Contrast basically the difference between the intensity
values of darker and brighter pixels.
 Contrast stretching is done in 3 ways,
 Multiplying each input pixel intensity value with a
constant scalar.
 Using histogram equivalent.
 Applying a transform which makes dark portion
darker by assigning slope of < 1 and bright portion
brighter by assigning slope of > 1.
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CONTRAST STRETCHING EXAMPLE
Transformation function Low contrast image
Contrast stretching image
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INTENSITY LEVEL SLICING
 Highlighting a specific range of gray levels in an image
often is desired.
 Gray level slicing function can either emphasize a group
intensities and diminish all other or it can emphasize a
group of gray levels and leave the rest alone.
23

INTENSITY LEVEL SLICING EXAMPLE
Input image Output image
25502550
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BIT PLANE SLICING
 Bit plane slicing the image is considered to be a stack of
binary images.
 Instead of highlighting gray-level ranges, highlighting
the contribution made to total image appearance by
specific bits might be desired.
 The images closes to the bottom are least significant and
the on top are most significant.
25

10110011
1
1
0
0
1
1
0
1
Bit-plane 0
(least significant)
Bit-plane 7
(most significant)
26

BIT PLANE SLICING EXAMPLE
Original image
Bit plane 7 Bit plane 6 Bit plane 4 Bit plane 1 27

THANK YOU !
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Point processing

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