gs & as, bahria university, islambad dip, spring 2012 1 spatial filtering: basics neighbourhood...
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GS & AS, Bahria University, IslambadDIP, Spring 2012 1
Spatial Filtering: Basics
Neighbourhood operations: Operate on a larger neighbourhood of pixels than point operations
Neighbourhood operations: Operate on a larger neighbourhood of pixels than point operations
Origin x
y Image f (x, y)
(x, y)Neighbourhood
Neighbourhoods are mostly a rectangle around a central pixel
Neighbourhoods are mostly a rectangle around a central pixel
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GS & AS, Bahria University, IslambadDIP, Spring 2012 2
Spatial Filtering: Basics
r s t
u v w
x y z
Origin x
y Image f (x, y)
eprocessed = v*e + r*a + s*b + t*c + u*d + w*f + x*g + y*h + z*i
FilterSimple 3*3
Neighbourhoode 3*3 Filter
a b c
d e f
g h i
Original Image Pixels
*
The above is repeated for every pixel in the original image to generate the filtered imageThe above is repeated for every pixel in the original image to generate the filtered image
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Spatial Filtering: Basics
Moving window (kernel) scans the 3x3 neighborhood of every pixel in the image
Original Image
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Spatial Filtering: Basics
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Spatial Filtering: Basics
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Spatial Filtering: Basics
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Spatial Filtering: Basics
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Spatial Filtering: Basics
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Spatial Filtering: Basics
Mask operation near the image border: Problem arises when part of the mask is located outside the image plane
Discard the problem pixels (e.g. 512x512 input 510x510 output if mask size is 3x3)
Discard the problem pixels (e.g. 512x512 input 510x510 output if mask size is 3x3)
Zero padding: Expand the input image by padding zeros (512x512 original image, 514x514 padded image, 512x512 output)
Zero padding is not recommended as it creates artificial lines or edges on the border
Zero padding: Expand the input image by padding zeros (512x512 original image, 514x514 padded image, 512x512 output)
Zero padding is not recommended as it creates artificial lines or edges on the border
Pixel replication: We normally use the gray levels of border pixels to fill up the expanded region (for 3x3 mask). For larger masks a border region equal to half of the mask size is mirrored on the expanded region.
Pixel replication: We normally use the gray levels of border pixels to fill up the expanded region (for 3x3 mask). For larger masks a border region equal to half of the mask size is mirrored on the expanded region.
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Spatial Filtering: Basics
The output intensity value at (x,y) depends not only on the input intensity value at (x,y) but also on the specified number of neighboring intensity values around (x,y)
Spatial masks (also called window, filter, kernel, template) are used and convolved over the entire image for local enhancement (spatial filtering)
The size of the masks determines the number of neighboring pixels which influence the output value at (x,y)
The values (coefficients) of the mask determine the nature and properties of enhancing technique
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Spatial Filtering: Basics
Given the 3×3 mask with coefficients: w1, w2,…, w9
The mask cover the pixels with gray levels: z1, z2,…, z9
z gives the output intensity value for the processed image (to be stored in a new array) at the location of z5 in the input image
z1 z2 z3
z4 z5 z6
z7 z8 z9
9
1 1 2 2 3 3 9 91
i ii
z z w z w z w z w z w
w1 w2 w3
w4 w5 w6
w7 w8 w9
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Smoothing Spatial Filters
For blurring/noise reduction
Blurring is usually used in preprocessing steps, e.g., to remove
small details from an image prior to object extraction, or to
bridge small gaps in lines or curves
Equivalent to Low-pass spatial filtering in frequency domain
because smaller (high frequency) details are removed based
on neighborhood averaging (averaging filters)
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Smoothing Spatial Filters
Simply average all of the pixels in a neighbourhood around a central value
Simply average all of the pixels in a neighbourhood around a central value
1/91/9
1/9
1/91/9
1/9
1/91/9
1/9
Simple averaging filter
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Spatial Filtering: Basics
1/91/9
1/9
1/91/9
1/9
1/91/9
1/9
Origin x
y Image f (x, y)
e = 1/9*106 + 1/9*104 + 1/9*100 + 1/9*108 + 1/9*99 + 1/9*98 + 1/9*95 + 1/9*90 + 1/9*85
= 98.3333
FilterSimple 3*3
Neighbourhood106
104
99
95
100 108
98
90 85
1/91/9
1/9
1/91/9
1/9
1/91/9
1/9
3*3 SmoothingFilter
104 100 108
99 106 98
95 90 85
Original Image Pixels
*
The above is repeated for every pixel in the original image to generate the smoothed image
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Smoothing Spatial Filters
Box Filter all coefficients are
equal
Weighted Average give more (less) weight to near
(away from) the output location
Consider the output pixel is positioned at the center
Consider the output pixel is positioned at the center
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Original image
Size: 500x500Smooth by 3x3 box filter
Smooth by 5x5 box filter
Smooth by 9x9 box filter
Smooth by 15x15 box filter
Smooth by 35x35 box filter
Notice how detail begins to disappearNotice how detail begins to disappear
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Order-Statistic Filtering
Output is based on order of gray levels in the masked area
Some simple neighbourhood operations include: Min: Set the pixel value to the minimum in the
neighbourhood Max: Set the pixel value to the maximum in the
neighbourhood Median: The median value of a set of numbers is the
midpoint value in that set
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Median Filtering
Median = ? 20
Particularly effective when The noise pattern consists of strong
impulse noise ( salt-and-pepper)
Particularly effective when The noise pattern consists of strong
impulse noise ( salt-and-pepper)
Sort the valuesDetermine the median
Sort the valuesDetermine the median
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Median Filtering
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GS & AS, Bahria University, IslambadDIP, Spring 2012
Sharpening Spatial Filters
Previously we have looked at smoothing filters which remove
fine detail
Sharpening spatial filters seek to highlight fine detail
Remove blurring from images
Highlight edges
Sharpening filters are based on spatial differentiation
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GS & AS, Bahria University, IslambadDIP, Spring 2012
Spatial Differentiation
Let’s consider a simple 1 dimensional example
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GS & AS, Bahria University, IslambadDIP, Spring 2012
Spatial Differentiation
A B
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GS & AS, Bahria University, IslambadDIP, Spring 2012
1st Derivative
The 1st derivative of a function is given by:
Its just the difference between subsequent values and measures the rate of change of the function
)()1( xfxfx
f
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GS & AS, Bahria University, IslambadDIP, Spring 2012
Image Strip
0
1
2
3
4
5
6
7
8
1st Derivative
-8
-6
-4
-2
0
2
4
6
8
5 5 4 3 2 1 0 0 0 6 0 0 0 0 1 3 1 0 0 0 0 7 7 7 7
-1 -1 -1 -1 -1 0 0 6 -6 0 0 0 1 2 -2 -1 0 0 0 7 0 0 0
1st Derivative1st Derivative
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GS & AS, Bahria University, IslambadDIP, Spring 2012
2nd Derivative
The 2nd derivative of a function is given by:
Simply takes into account the values both before and after the current value
)(2)1()1(2
2
xfxfxfx
f
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GS & AS, Bahria University, IslambadDIP, Spring 2012
Image Strip
0
1
2
3
4
5
6
7
8
5 5 4 3 2 1 0 0 0 6 0 0 0 0 1 3 1 0 0 0 0 7 7 7 7
2nd Derivative2nd Derivative
2nd Derivative
-15
-10
-5
0
5
10
-1 0 0 0 0 1 0 6 -12 6 0 0 1 1 -4 1 1 0 0 7 -7 0 0
-1 -1 -1 -1 -1 0 0 6 -6 0 0 0 1 2 -2 -1 0 0 0 7 0 0 0
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GS & AS, Bahria University, IslambadDIP, Spring 2012
2nd Derivative for Image Enhancement
The 2nd derivative is more useful for image enhancement than the 1st derivative - Stronger response to fine detail
The 2nd derivative is more useful for image enhancement than the 1st derivative - Stronger response to fine detail
We will come back to the 1st order derivative later onWe will come back to the 1st order derivative later on
The first sharpening filter we will look at is the LaplacianThe first sharpening filter we will look at is the Laplacian
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Laplacian Filter
y
f
x
ff
2
2
2
22
),(2),1(),1(2
2
yxfyxfyxfx
f
The Laplacian is defined as follows:
),(2)1,()1,(2
2
yxfyxfyxfy
f
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GS & AS, Bahria University, IslambadDIP, Spring 2012
Laplacian Filter
So, the Laplacian can be given as follows:
),1(),1([2 yxfyxff )]1,()1,( yxfyxf
),(4 yxf
0 1 0
1 -4 1
0 1 0
Can we implement it using a filter/ mask?
Can we implement it using a filter/ mask?
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GS & AS, Bahria University, IslambadDIP, Spring 2012
Laplacian Filter
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GS & AS, Bahria University, IslambadDIP, Spring 2012
Laplacian Filter
Applying the Laplacian to an image we get a new image that highlights edges and other discontinuities
OriginalImage
LaplacianFiltered Image
LaplacianFiltered Image
Scaled for Display
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GS & AS, Bahria University, IslambadDIP, Spring 2012
Laplacian Image Enhancement
The result of a Laplacian filtering is not an enhanced image
The result of a Laplacian filtering is not an enhanced image
LaplacianFiltered Image
Scaled for Display2
52
5
( , ) , 0( , )
( , ) , 0
f x y f wg x y
f x y f w
To generate the final enhanced imageTo generate the final enhanced image
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Laplacian Image Enhancement
In the final sharpened image edges and fine detail are much more obvious
- =
OriginalImage
LaplacianFiltered Image
SharpenedImage
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GS & AS, Bahria University, IslambadDIP, Spring 2012
Simplified Image Enhancement
The entire enhancement can be combined into a single filtering operation
),1(),1([),( yxfyxfyxf )1,()1,( yxfyxf
)],(4 yxf
fyxfyxg 2),(),(
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GS & AS, Bahria University, IslambadDIP, Spring 2012
Simplified Image Enhancement
The entire enhancement can be combined into a single filtering operation
fyxfyxg 2),(),(
),1(),1(),(5 yxfyxfyxf )1,()1,( yxfyxf
0 -1 0
-1 5 -1
0 -1 0
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Simplified Image Enhancement
This gives us a new filter which does the whole job for us in one step
0 -1 0
-1 5 -1
0 -1 0
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GS & AS, Bahria University, IslambadDIP, Spring 2012
Use of first derivatives for image enhancement: The Gradient
The gradient of a function f(x,y) is defined as
y
fx
f
G
G
y
xf
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GS & AS, Bahria University, IslambadDIP, Spring 2012
The magnitude of this vector is given by:
)f( magf
21
22yx GG
21
22
y
f
x
f
For practical reasons this can be simplified as:
yx GGf
Use of first derivatives for image enhancement: The Gradient
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GS & AS, Bahria University, IslambadDIP, Spring 2012
There is some debate as to how best to calculate these gradients
Gradient Operators
Simplest OperatorSimplest Operator
8 5 6 5
2 28 5 6 5
8 5 6 5
( ), ( )
( ) ( )
( ) ( )
f fz z z z
x y
f z z z z
f z z z z
xy
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GS & AS, Bahria University, IslambadDIP, Spring 2012
Gradient Operators
Prewitt OperatorPrewitt Operator
7 8 9 1 2 3
3 6 9 1 4 7
( ) ( )
( ) ( )
f z z z z z z
z z z z z z
-1 -1 -1
0 0 0
1 1 1
f
x
-1 0 1
-1 0 1
-1 0 1
f
y
Extract horizontal edges Extract vertical edges
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-1 -2 -1
0 0 0
1 2 1
f
x
-1 0 1
-2 0 2
-1 0 1
f
y
Extract horizontal edges
7 8 9 1 2 3
3 6 9 1 4 7
( 2 ) ( 2 )
( 2 ) ( 2 )
f z z z z z z
z z z z z z
Emphasize more the current point (x direction)
Emphasize more the current point (y
direction) Pixel Arrangement
Gradient Operators
Extract vertical edges
Sobel OperatorSobel Operator
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Sobel Operator: Example
Sobel filters are typically used for edge detection
An image of a contact lens which is enhanced in order to make defects more obvious