6 spatial filtering p2

Digital Image Processing

Image Enhancement
(Spatial Filtering 2)

Course Website: http://www.comp.dit.ie/bmacnamee

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Contents
In this lecture we will look at more spatial
filtering techniques
– Spatial filtering refresher
– Sharpening filters
• 1st derivative filters
• 2nd derivative filters
– Combining filtering techniques

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Spatial Filtering Refresher
Origin x
a b c r s t
d
g
e
h
f
i
* u
x
v
y
w
z
Original Image Filter
Simple 3*3 Pixels
e 3*3 Filter
Neighbourhood
eprocessed = v*e +
r*a + s*b + t*c +
u*d + w*f +
y Image f (x, y) x*g + y*h + z*i

The above is repeated for every pixel in the
original image to generate the smoothed image

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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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Spatial Differentiation
Differentiation measures the rate of change of
Images taken from Gonzalez & Woods, Digital Image Processing (2002)

a function
Let’s consider a simple 1 dimensional
example

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A
B
Spatial Differentiation

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1st Derivative
The formula for the 1st derivative of a
function is as follows:
∂f
= f ( x + 1) − f ( x)
∂x
It’s just the difference between subsequent
values and measures the rate of change of
the function

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1st Derivative (cont…)

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

0 -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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2nd Derivative
The formula for the 2nd derivative of a
function is as follows:
∂ f
2
= f ( x + 1) + f ( x − 1) − 2 f ( x)
∂ x
2

Simply takes into account the values both
before and after the current value

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2nd Derivative (cont…)

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 0 0 0 0 1 0 6 -12 6 0 0 1 1 -4 1 1 0 0 7 -7 0 0

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Using Second Derivatives For Image
30 Enhancement
The 2nd derivative is more useful for image
enhancement than the 1st derivative
– Stronger response to fine detail
– Simpler implementation

The first sharpening filter we will look at is
the Laplacian
– Isotropic
– One of the simplest sharpening filters
– We will look at a digital implementation

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The Laplacian
The Laplacian is defined as follows:
∂ f ∂ f
2 2
∇ f = 2 + 2
2

∂ x ∂ y
where the partial 1st order derivative in the x
direction is defined as follows:
∂ f
2
= f ( x + 1, y ) + f ( x − 1, y ) − 2 f ( x, y )
∂ x
2

and in the y direction as follows:
∂ f
2
= f ( x, y + 1) + f ( x, y − 1) − 2 f ( x, y )
∂ y
2

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The Laplacian (cont…)
So, the Laplacian can be given as follows:
∇ f = [ f ( x + 1, y ) + f ( x − 1, y )
2

+ f ( x, y + 1) + f ( x, y − 1)]
− 4 f ( x, y )
We can easily build a filter based on this
0 1 0

1 -4 1

0 1 0

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The Laplacian (cont…)
Applying the Laplacian to an image we get a

new image that highlights edges and other
discontinuities

Original Laplacian Laplacian
Image Filtered Image Filtered Image
Scaled for Display

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But That Is Not Very Enhanced!
The result of a Laplacian filtering

is not an enhanced image
We have to do more work in
order to get our final image
Subtract the Laplacian result
Laplacian
from the original image to Filtered Image
Scaled for Display
generate our final sharpened
enhanced image
g ( x, y ) = f ( x, y ) − ∇ f
2

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Laplacian Image Enhancement

- =
Original Laplacian Sharpened
Image Filtered Image Image

In the final sharpened image edges and fine
detail are much more obvious


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Laplacian Image Enhancement

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Simplified Image Enhancement
The entire enhancement can be combined
into a single filtering operation
g ( x, y ) = f ( x, y ) − ∇ f
2

= f ( x, y ) − [ f ( x + 1, y ) + f ( x − 1, y )
+ f ( x, y + 1) + f ( x, y − 1)
− 4 f ( x, y )]
= 5 f ( x, y ) − f ( x + 1, y ) − f ( x − 1, y )
− f ( x, y + 1) − f ( x, y − 1)

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Simplified Image Enhancement (cont…)

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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Simplified Image Enhancement (cont…)

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Variants On The Simple Laplacian
There are lots of slightly different versions of

the Laplacian that can be used:
0 1 0 1 1 1
Simple Variant of
1 -4 1 1 -8 1
Laplacian Laplacian
0 1 0 1 1 1

-1 -1 -1

-1 9 -1

-1 -1 -1

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Simple Convolution Tool In Java
A great tool for testing out different filters
– From the book “Image Processing tools in
Java”
– Available from webCT later on today
– To launch: java ConvolutionTool Moon.jpg

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1st Derivative Filtering
Implementing 1st derivative filters is difficult in
practice
For a function f(x, y) the gradient of f at
coordinates (x, y) is given as the column
vector:
 ∂f 
Gx   ∂x 
∇f =   =  ∂f 
G y   
 ∂y 
 

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1st Derivative Filtering (cont…)
The magnitude of this vector is given by:
∇f = mag (∇f )
[
= G +G2
x
2
y ] 1
2

1
 ∂f   ∂f  
2 2 2

=   +   
 ∂x   ∂y  
  

For practical reasons this can be simplified as:
∇f ≈ G x + G y

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1st Derivative Filtering (cont…)
There is some debate as to how best to
calculate these gradients but we will use:
∇f ≈ ( z7 + 2 z8 + z9 ) − ( z1 + 2 z 2 + z3 )
+ ( z3 + 2 z6 + z9 ) − ( z1 + 2 z 4 + z7 )
which is based on these coordinates
z1 z2 z3

z4 z5 z6

z7 z8 z9

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Sobel Operators
Based on the previous equations we can
derive the Sobel Operators
-1 -2 -1 -1 0 1

0 0 0 -2 0 2

1 2 1 -1 0 1

To filter an image it is filtered using both
operators the results of which are added
together

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Sobel Example

An image of a
contact lens which
is enhanced in
order to make
defects (at four
and five o’clock in
the image) more
obvious

Sobel filters are typically used for edge
detection

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1st & 2nd Derivatives
Comparing the 1st and 2nd derivatives we
can conclude the following:
– 1st order derivatives generally produce thicker
edges
– 2nd order derivatives have a stronger
response to fine detail e.g. thin lines
– 1st order derivatives have stronger response
to grey level step
– 2nd order derivatives produce a double
response at step changes in grey level

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Summary
In this lecture we looked at:
– Sharpening filters
• 1st derivative filters
• 2nd derivative filters
– Combining filtering techniques

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Combining Spatial Enhancement
30 Methods
Successful image

enhancement is typically
not achieved using a single
operation
Rather we combine a range
of techniques in order to
achieve a final result
This example will focus on
enhancing the bone scan to
the right

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(a)
Laplacian filter of
bone scan (a)
(b)
Sharpened version of
bone scan achieved (c)
by subtracting (a)
and (b) Sobel filter of bone
scan (a) (d)

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Result of applying a (h)
power-law trans. to

Sharpened image (g)
which is sum of (a)
and (f) (g)
The product of (c)
and (e) which will be (f)
used as a mask
(e)

Image (d) smoothed with
a 5*5 averaging filter

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Compare the original and final images

6 spatial filtering p2

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6 spatial filtering p2