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Department of Physics and Astronomy

DIGITAL IMAGE PROCESSING

Course 3624

Topic 6 - Image Filtering - I

Professor Bob Warwick

6. Image Filtering I: Spatial Domain Filtering

The goal of spatial filtering is typically either to ( a) reduce the impact of noise in the image via image smoothing or (b) to sharpen the detail within the image.

Both processes involve the suppression and/or enhancement of particular spatial frequency components in the image (see Topic 8).

Point process (eg. Mapping) Neighbourhood process (eg. Spatial filtering)

Procedure h mask, filter, operator etc.. (usually with a 3 x 3 or 5 x 5 or 7 x 7 etc.. format) i h

00 h

10 h

01 h

11

å i=n-1 i=0

å j=n-1 j=0 h ij

Calculate for all x & y h

20

(ii) Compute a new image g xy from the old image f xy via:

å i=n-1 å i=0 j=n-1 h ij f x-m+i, y-m+j j=0 g xy

=

For a mask of dimension n x n (n odd), where m = (n-1)/2 is the central element.

h

21

Note that the denominator is a normalisation factor – sometimes not applicable!

j h

02 h

12 h

22

Computing the 2-d Result

original 3x3 average

3

6.1 Smoothing Filters

Smoothing filters involve calculating the average value (with some defined weighting) within the masked region.

Assuming a 3 x 3 format, some possibilities are:

1 1 1 Replaces the original value with

1 1 1

1 1 1 the average of 9 values (the central

1 1 1

1 1 1 pixel plus its 8 nearest neighbours)

1 1 1

1 1 1

Double weighting of the central pixel

1 2 1

1 2 1

1 1 1

1 1 1

0 1 0

0 1 0

1 1 1

Replaces the original value with the average of 5 values (the central

1 1 1

0 1 0 pixel plus its 4 nearest neighbours)

0 1 0

Smoothing Examples: Original Images

Author: Richard Alan Peters II

1

9

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Smoothing Examples: 3

3 Blur

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Smoothing Examples: 5

5 Blur

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 1

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Smoothing Examples: 9

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Smoothing Examples: 17

17 Blur

Image Smoothing Examples

Original Smoothed

Noise Suppression Example

Noise Suppression Example

Calculating the effective noise reduction

P(f)

Gaussian Distribution

P(f)

=

1 s

2 p m

, s e

-

(f

m

)

2

2 s 2 mean and standard deviation

Example

Applying a 3 x 3 smoothing filter with unit coefficients results in a factor 3 reduction in the noise.

f g xy m g s g

=

=

=

å

m f s f

9

9

9 f xy

The Median Filter

Smoothing filters can be used to reduce the impact of the noise in an image but also cause unwanted blurring . The larger the dimension of the smoothing mask the greater the noise reduction factor but also the greater the blurring.

Non-linear filters, such as the Median Filter , can to some degree

“decouple” these two effects – but in a way which is hard to quantify.

The median filter would replace the central value with the median value contained within the mask (or window) region.

152

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Suppression of Impulsive Noise

Smoothing Filter Median Filter

Suppression of Impulsive Noise

6.2 Sharpening Filters

The objective is to enhance the detail in the image by accentuating edges and boundaries.

Smoothing  Averaging Sharpening  Differencing

Continuous variables

In practice with discrete variables differencing is equivalent to differentiation and both the 1 st and 2 nd differentials can be used to mark the position in an image where there are rapid changes in gray level

(ie edges).

f ( x ) f

¢

( x ) f

¢¢

( x ) f ( x )

- ¢¢

( x ) f ( x )

+ ¢

( x )

Types of Filters Used in Image Sharpening - I

Pairs of masks of various formats which give the vertical (a xy

) and horizontal (b xy

) gradients:

(i) The Roberts Operators

-

1

1 a xy

=f x+1,y

-f xy

-

1 1 b xy

=f x,y+1

-f xy

Where the final gradient image is calculated as:

(ii)

-

1

0

1 a xy

=f x+1,y

-f x-1,y

-

1 0 1 b xy

=f x,y+1

-f x,y-1 g or xy

= a

2 xy

+b

2 xy g xy

= a xy

+ b xy

(iii) The Prewitt Operators

-

1

-

1

-

1

0 0 0

1 1 1

-

1 0 1

-

1 0 1

-

1 0 1

Image Sharpening Examples

Consider an image consisting of:

… 0 0 0 0 1 1 1 …..

… 0 0 0 0 1 1 1 …..

… 0 0 0 0 1 1 1 …..

… 0 0 0 0 1 1 1 …..

Applying the following filters gives:

(i) -

1

-

1

-

1

0 0 0

1 1 1

… 0 0 0 0 0 0 0 …..

… 0 0 0 0 0 0 0 …..

… 0 0 0 0 0 0 0 …..

… 0 0 0 0 0 0 0 …..

(ii) -

1 0 1

-

1 0 1

-

1 0 1

… 0 0 0 3 3 0 0 …..

… 0 0 0 3 3 0 0 …..

… 0 0 0 3 3 0 0 …..

… 0 0 0 3 3 0 0 …..

Calculating the Gradient Image

Lena

Calculating the Gradient Image

The Sobel Operators

-

1

-

2

-

1

0 0 0

1 2 1

-

1 0 1

-

2 0 2

-

1 0 1

Types of Filters Used in Image Sharpening - II

Masks which give the 2 nd differential or Laplacian image

(i)

For discrete data

: f x

' = f x

-f x-1 f x

'' = f

' x

-f

' x-1

=

(f x

-f x-1

)

-

(f x-1

-f x-2

)

= f x

-

2f x-1

+ f x-2

Þ f x-1

+

2f x

f x+1

(ii)

(iii)

-

1 2

-

1

0

-

1 0

-

1 4

-

1

0

-

1 0

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1

-

1

-

1

-

1 8

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1

-

1

-

1

-

1

-

1

2

-

1

The 1-d and 2-d Laplacian Mask in Action f xy

510

255

0

-255

2f xy

-f x,y-1

-f x,y+1

2f xy

-f x-1,y

-f x+1,y

-1 2 -1

-1

2

-1

4f xy

f x-1,y

-f x+1,y

f x,y-1

-f x,y+1

-1

-1

4 -1

-1

The Image Sharpening Process

In image sharpening the usual approach is to add the derived ‘gradient’ or ‘Laplacian’ image to the original image in a process known as high frequency emphasis .

Thus:

Final image = original image + gradient or Laplacian image

For the Laplacian case this can be achieved in one step by modifying the 2-d mask to:

0

-

1 0

-

1 5

-

1

0

-

1 0 or

-

1

-

1

-

1

-

1 9

-

1

-

1

-

1

-

1

Image Sharpening Examples

Consider an image consisting of:

… 0 0 0 0 1 1 1 …..

… 0 0 0 0 1 1 1 …..

… 0 0 0 0 1 1 1 …..

… 0 0 0 0 1 1 1 …..

Applying the following filters gives:

(i)

-

1

-

1

-

1

-

1 8

-

1

-

1

-

1

-

1

… 0 0 0 -3 3 0 0 …..

… 0 0 0 -3 3 0 0 …..

… 0 0 0 -3 3 0 0 …..

… 0 0 0 -3 3 0 0 …..

(ii) -

1

-

1

-

1

-

1 9

-

1

-

1

-

1

-

1

… 0 0 0 -3 4 1 1 …..

… 0 0 0 -3 4 1 1 …..

… 0 0 0 -3 4 1 1 …..

… 0 0 0 -3 4 1 1 …..

Image Sharpening Examples

The Mathematical Process of Spatial Filtering

Consider the 1-d case of spatial filtering with an n element mask

(n odd), where m= (n-1)/2 and ignoring the normalization factor.

g x

=

å

i=n-1 i=0 h i f x-m+i

(1) x f

0 f

1 f x h

0 h m h n-1 g x

=

å h h i' f x+i' g x

=

å h h i'' f x-i'' g x

=

å h h x

a f a

(2)

(3)

(4) i i' i''

Equation 4 is the discrete version of the CONVOLUTION INTEGRAL.

SPATIAL FILTERING IS A

CONVOLUTION PROCESS i'=i-m i''=-i' a = x-i' ' g ( x )=

ò

¥

-¥ f ( a

) h ( x

a

) d a

Shorthand versions : g ( x )

= f ( x )

* h ( x ) g x

= f x

* h x f

N-1

The Mathematical Process of “Convolution”

Practical Considerations

(i) Edge Errors

For an n x n mask there is a (n-1)/2 border to the new image where exact values cannot be calculated. Solutions are:

(a) Reduce the dimensions of the new image (usually impractical)

(b) Calculate approximate values for the border pixels

(c) Compute the cyclic convolution (beyond our scope).

(ii) Separability

A 2-d mask is separable if the successive application of two 1-d masks gives the equivalent result to the application of the 2-d mask.

For example, considered as matrices:

ç

è

ç

ç

æ

1 1 1

1 1 1

1 1 1

÷

ø

÷

÷

ö

º

(

1 1 1

)

*

ç

è

ç

ç

æ

1

1

1

÷

ø

÷

÷

ö

Therefore this smoothing mask is separable.

There are computational advantages if the mask is separable.

Note: Both gradient and Laplacian filters can also be used in

EDGE DETECTION – see topic 10

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