Transcript Document

Digital Image Processing
Session 3
Dr. Ghassabi
[email protected]
Tehran shomal University
Spring 2015
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Outline
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Introduction
Digital Image Fundamentals
Intensity Transformations and Spatial Filtering
Filtering in the Frequency Domain
Image Restoration and Reconstruction
Color Image Processing
Wavelets and Multi resolution Processing
Image Compression
Morphological Operation
Object representation
Object recognition
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Outline
Chapter 3
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Background
Some Basic Intensity Transformation Functions
Histogram Processing
Fundamentals of Spatial Filtering
Smoothing Spatial Filters
Sharpening Spatial Filters
Combining Spatial Enhancement Tools
Image Enhancement
• Methods
– Spatial Domain:
• Linear
• Nonlinear
– Frequency Domain:
• Linear
• Nonlinear
IE in Spatial Domain
g  x, y   T  f  x, y 
Transformation
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For 11 neighborhood: s  T  r 
– Contrast Enhancement/Stretching/Point process
For w w neighborhood:
– Filtering/Mask/Kernel/Window/Template Processing
IE in Spatial Domain
Negative
nth root
Log
Identity
nth power
Inverse Log
Input gray level, r
Some Basic Intensity
Transformation Functions
Image Negatives
• Image Negatives:
s  L 1  r
Image Negatives
y
L
0
y=L-x
L
x
Log Transformation
Log Transformation
Range Compression
y
y  c log10 (1  x)
0
L
x
c=100
Power-Law(Gamma) Transformations
s  c r   

Power-Law(Gamma) Transformations
Gamma Correction:
r1.8
r
1.8
2.5
 1
2.5
r1.8
2.5
Power-Law(Gamma) Transformations
Original
 =0.6
 =0.4
 =0.3
Power-Law(Gamma) Transformations
(Effect of decreasing gamma)
Original
 =3
 =4
 =5
Power-Law(Gamma) Transformations
(Effect of decreasing gamma)
Piecewise-Linear Transformation Functions
• Contrast Stretching
• Contrast slicing
• Bite-Plane slicing
Contrast Stretching
y
x
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y    ( x  a )  ya
  ( x  b)  y
b
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0 xa
a xb
b xL
yb
ya
0
a b
a  50, b  150,  0.2,   2,   1, ya  30, yb  200
L
x
Contrast stretching
Original
C. S.
THR.
Contrast Stretching
Clipping
y
0
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y   ( x  a)
  (b  a )
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0 xa
a xb
bxL
0
a b
a  50, b  150,   2
L
x
Gray-level Slicing
Gray-level Slicing
Gray-level Slicing
Gray-level Slicing
Gray-level Slicing
Bit-plane Slicing
Highlighting the contribution made to total image appearance by specific bits
Suppose each pixel is represented by 8 bits
Higher-order bits contain the majority of the visually significant data
Useful for analyzing the relative importance played by each bit of the image
Bit-plane Slicing
Bit-plane Slicing
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
Bit-plane Slicing - Fractal Image
Bit-plane Slicing - Fractal Image
Bit-plane 7
Bit-plane 6
Bit-plane 5
Bit-plane 4
Bit-plane 3
Bit-plane 2
Bit-plane 1
Bit-plane 0
Bit-plane Slicing
Histogram Processing
Enhancement based on statistical Properties: Local, Global
Histogram Definition
h(rk)=nk
Where rk is the kth gray level and nk is the number of pixels in the image having gray level rk
Normalized histogram:
P(rk)=nk/n
Histogram of an image represents the relative frequency of occurrence of various gray levels in the
image
Histogram Examples
• Histogram Visual Meaning:
– Dark
– Bright
– Low Contrast
– High Contrast
Histogram Example
Histogram Example
Histogram Modification
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Histogram Stretching
Histogram Shrink
Histogram Sliding
Histogram Stretching
Histogram Stretching
Histogram Stretching
Histogram Shrinking
Histogram Shrinking
Histogram Sliding