Lecture 6 Image Enhancement in Spatial Domain › CMS › AUT2010 › cpDIPbs › ... · Lecture 6...
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Digital Image ProcessingDigital Image Processing
Lecture 6 Lecture 6
Image Enhancement in Spatial Image Enhancement in Spatial
DomainDomain
Fall 2010Fall 2010
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Lecture # 6 2
Spatial FilteringSpatial Filtering
A spatial filter consists of (a) a neighborhood, and (b) apredefined operation
Linear spatial filtering of an image of size MxN with a filter of size mxn is given by the expression
( , ) ( , ) ( , )a b
s a t b
g x y w s t f x s y t=− =−
= + +∑ ∑
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Lecture # 6 3
Spatial FilteringSpatial Filtering
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Lecture # 6 4
Spatial CorrelationSpatial Correlation
The correlation of a filter ( , ) of size
with an image ( , ), denoted as ( , ) ( , )
w x y m n
f x y w x y f x y
×
( , ) ( , ) ( , ) ( , )a b
s a t b
w x y f x y w s t f x s y t=− =−
= + +∑ ∑
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Lecture # 6 5
Spatial ConvolutionSpatial Convolution
The convolution of a filter ( , ) of size
with an image ( , ), denoted as ( , ) ( , )
w x y m n
f x y w x y f x y
×
( , ) ( , ) ( , ) ( , )a b
s a t b
w x y f x y w s t f x s y t=− =−
= − −∑ ∑
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Lecture # 6 6
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Lecture # 6 7
Smoothing Spatial FiltersSmoothing Spatial Filters
Smoothing filters are used for blurring and for noise reduction
Blurring is used in removal of small details and bridging ofsmall gaps in lines or curves
Smoothing spatial filters include linear filters and nonlinear filters.
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Lecture # 6 8
Spatial Smoothing Linear FiltersSpatial Smoothing Linear Filters
The general implementation for filtering an M N image
with a weighted averaging filter of size m n is given
( , ) ( , )
( , )
( , )
where 2 1
a b
s a t b
a b
s a t b
w s t f x s y t
g x y
w s t
m a
=− =−
=− =−
×
×
+ +
=
= +
∑ ∑
∑ ∑
, 2 1.n b= +
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Lecture # 6 9
Smoothing Spatial FiltersSmoothing Spatial Filters
► used for blurring and for noise reduction
► blurring is used in preprocessing steps, such as
� removal of small details from an image prior to object extraction
� bridging of small gaps in lines or curves
► noise reduction can be accomplished by blurring with a linear filter and also by a nonlinear filter
► replacing the value of every pixel in an image by the average of the gray levels in the neighborhood will reduce the “sharp” transitions in gray levels.
► sharp transitions
� random noise in the image
� edges of objects in the image
► thus, smoothing can reduce noises (desirable) and blur edges (undesirable)
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Lecture # 6 10
Two Smoothing Averaging Filter MasksTwo Smoothing Averaging Filter Masks
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Lecture # 6 11
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Lecture # 6 12
Example: Gross Representation of ObjectsExample: Gross Representation of Objects
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Lecture # 6 13
OrderOrder--statistic (Nonlinear) Filtersstatistic (Nonlinear) Filters
— Nonlinear
— Based on ordering (ranking) the pixels contained in the filter mask
— Replacing the value of the center pixel with the value determined by the ranking result
E.g., median filter, max filter, min filter
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Lecture # 6 14
Median FiltersMedian Filters
► replaces the value of a pixel by the median of the gray levels in the neighborhood of that pixel (the original value of the pixel is included in the computation of the median)
► quite popular because for certain types of random noise (impulse noise � salt and pepper noise) , they provide excellent noise-reduction capabilities, with considering less blurring than linear smoothing filters of similar size.
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Lecture # 6 15
Example: Use of Median Filtering for Noise ReductionExample: Use of Median Filtering for Noise Reduction
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Lecture # 6 16
Sharpening Spatial FiltersSharpening Spatial Filters
► Foundation
► Laplacian Operator
► Unsharp Masking and Highboost Filtering
► Using First-Order Derivatives for Nonlinear Image Sharpening — The Gradient
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Lecture # 6 17
Sharpening Spatial FiltersSharpening Spatial Filters
► to highlight fine detail in an image
► or to enhance detail that has been blurred, either in error or as a natural effect of a particular method of image acquisition.
► Blurring Vs. Sharpening
� as we know that blurring can be done in spatial domain by pixel averaging in a neighbors
� since averaging is analogous to integration
� thus, we can guess that the sharpening must be accomplished by spatial differentiation.
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Lecture # 6 18
Derivative operatorDerivative operator
► the strength of the response of a derivative operator is proportional to the degree of discontinuity of the image at the point at which the operator is applied.
► thus, image differentiation
� enhances edges and other discontinuities (noise)
� deemphasizes area with slowly varying gray-level values.
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Lecture # 6 19
Sharpening Spatial Filters: FoundationSharpening Spatial Filters: Foundation
► The first-order derivative of a one-dimensional function f(x) is the difference
► The second-order derivative of f(x) as the difference
( 1) ( )f
f x f xx
∂= + −
∂
2
2( 1) ( 1) 2 ( )
ff x f x f x
x
∂= + + − −
∂
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Lecture # 6 20
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Lecture # 6 21
Sharpening Spatial Filters: Laplace OperatorSharpening Spatial Filters: Laplace Operator
The second-order isotropic derivative operator is the Laplacian for a function (image) f(x,y)
2 22
2 2
f ff
x y
∂ ∂∇ = +
∂ ∂2
2( 1, ) ( 1, ) 2 ( , )
ff x y f x y f x y
x
∂= + + − −
∂
2
2( , 1) ( , 1) 2 ( , )
ff x y f x y f x y
y
∂= + + − −
∂
2 ( 1, ) ( 1, ) ( , 1) ( , 1)
- 4 ( , )
f f x y f x y f x y f x y
f x y
∇ = + + − + + + −
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Lecture # 6 22
Sharpening Spatial Filters: Laplace OperatorSharpening Spatial Filters: Laplace Operator
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Lecture # 6 23
Sharpening Spatial Filters: Laplace OperatorSharpening Spatial Filters: Laplace Operator
Image sharpening in the way of using the Laplacian:
2
2
( , ) ( , ) ( , )
where,
( , ) is input image,
( , ) is sharpenend images,
-1 if ( , ) corresponding to Fig. 3.37(a) or (b)
and 1 if either of the other two filters is us
g x y f x y c f x y
f x y
g x y
c f x y
c
= + ∇
= ∇
= ed.
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Lecture # 6 24
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Lecture # 6 25
UnsharpUnsharp Masking and Masking and HighboostHighboost FilteringFiltering
► Unsharp masking
Sharpen images consists of subtracting an unsharp (smoothed) version of an image from the original image
e.g., printing and publishing industry
► Steps
1. Blur the original image
2. Subtract the blurred image from the original
3. Add the mask to the original
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Lecture # 6 26
UnsharpUnsharp Masking and Masking and HighboostHighboost FilteringFiltering
Let ( , ) denote the blurred image, unsharp masking is
( , ) ( , ) ( , )
Then add a weighted portion of the mask back to the original
( , ) ( , ) * ( , )
mask
mask
f x y
g x y f x y f x y
g x y f x y k g x y
= −
= + 0k ≥
when 1, the process is referred to as highboost filtering.k >
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Lecture # 6 27
UnsharpUnsharp Masking: DemoMasking: Demo
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Lecture # 6 28
UnsharpUnsharp Masking and Masking and HighboostHighboost Filtering: ExampleFiltering: Example
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Lecture # 6 29
Image Sharpening based on FirstImage Sharpening based on First--Order DerivativesOrder Derivatives
For function ( , ), the gradient of at coordinates ( , )
is defined as
grad( )x
y
f x y f x y
f
g xf f
fg
y
∂ ∂ ∇ ≡ ≡ = ∂ ∂
2 2
The of vector , denoted as ( , )
( , ) mag( )x y
magnitude f M x y
M x y f g g
∇
= ∇ = +Gradient Image
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Lecture # 6 30
Image Sharpening based on FirstImage Sharpening based on First--Order DerivativesOrder Derivatives
2 2
The of vector , denoted as ( , )
( , ) mag( )x y
magnitude f M x y
M x y f g g
∇
= ∇ = +
( , ) | | | |x y
M x y g g≈ +
zz99zz88zz77
zz66zz55zz44
zz33zz22zz11
8 5 6 5( , ) | | | |M x y z z z z= − + −
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Lecture # 6 31
Image Sharpening based on FirstImage Sharpening based on First--Order DerivativesOrder Derivatives
zz99zz88zz77
zz66zz55zz44
zz33zz22zz11
9 5 8 6
Roberts Cross-gradient Operators
( , ) | | | |M x y z z z z≈ − + −
7 8 9 1 2 3
3 6 9 1 4 7
Sobel Operators
( , ) | ( 2 ) ( 2 ) |
| ( 2 ) ( 2 ) |
M x y z z z z z z
z z z z z z
≈ + + − + +
+ + + − + +
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Lecture # 6 32
Image Sharpening based on FirstImage Sharpening based on First--Order DerivativesOrder Derivatives
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Lecture # 6 33
ExampleExample
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Lecture # 6 34
Example:
Combining Spatial Enhancement Methods
Goal:
Enhance the image by sharpening it and by bringing out more of the skeletal detail
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Lecture # 6 35
Example:
Combining Spatial Enhancement Methods
Goal:
Enhance the image by sharpening it and by bringing out more of the skeletal detail