Digital Image Processing

Image Enhancement

21/11/15

Duong Anh Duc - Digital Image Processing

1

Image Enhancement

To process an image so that output is “visually better”

than the input, for a specific application.

Enhancement is therefore, very much dependent on

the particular problem/image at hand.

Enhancement can be done in either:

– Spatial domain: operate on the original image

g(m,n) = T[f(m,n)]

– Frequency domain: operate on the DFT of the original image

G(u,v) = T[F(u,v)],

where

F(u,v) = F[f(m,n)], and G(u,v) = F [g(m,n)],

21/11/15

Duong Anh Duc - Digital Image Processing

2

Image Enhancement Techniques

Point Operations

•

•

•

•

•

•

•

Image Negative

Contrast

Stretching

Compression of

dynamic range

Graylevel slicing

Image

Subtraction

Image Averaging

Histogram

operations

21/11/15

Mask Operations

•

•

•

•

•

Smoothing

operations

Median Filtering

Sharpening

operations

Derivative

operations

Histogram

operations

Transform Operations

Coloring Operations

•

•

•

•

•

•

•

Low pass

Filtering

Hi pass Filtering

Band pass

Filtering

Homomorphic

Filtering

Histogram

operations

Duong Anh Duc - Digital Image Processing

False Coloring

Full color

Processing

3

Point Operations

Output pixel value g(m, n) at pixel (m, n) depends only on the input

pixel value at f(m, n) at (m, n) (and not on the neighboring pixel

values).

We normally write s = T(r), where s is the output pixel value and r is the

input pixel value.

T is any increasing function that maps [0,1] into [0,1].

21/11/15

Duong Anh Duc - Digital Image Processing

4

Image Negative

T(r) = s = L-1-r, L: max grayvalue

21/11/15

Duong Anh Duc - Digital Image Processing

5

Negative Image

21/11/15

Duong Anh Duc - Digital Image Processing

6

Contrast Stretching

Increase the dynamic range of grayvalues in the input image.

Suppose you are interested in stretching the input intensity values in the

interval [r1, r2]:

Note that (r1- r2) < (s1- s2). The grayvalues in the range [r1, r2] is stretched into

the range [s1, s2].

21/11/15

Duong Anh Duc - Digital Image Processing

7

Contrast Stretching

Special cases:

– Thresholding or

binarization

r1 = r2 , s1 = 0 and s2 = 1

– Useful when we are

only interested in the

shape of the objects

and on on their actual

grayvalues.

21/11/15

Duong Anh Duc - Digital Image Processing

8

Contrast Stretching

21/11/15

Duong Anh Duc - Digital Image Processing

9

Contrast Stretching

Special cases (cont.):

– Gamma correction:

S1 = 0, S2 = 1 and

0, r

g

T r

21/11/15

r1

r r1

, r1 r

r2 r1

1, r r2

r2

Duong Anh Duc - Digital Image Processing

10

Contrast Stretching

Gamma correction

21/11/15

Duong Anh Duc - Digital Image Processing

11

Compression of Dynamic

Range

When the dynamic range of the input

grayvalues is large compared to that of

the display, we need to “compress” the

grayvalue range --- example: Fourier

transform magnitude.

Typically we use a log scale.

s = T(r) = c log(1+ r )

21/11/15

Duong Anh Duc - Digital Image Processing

12

Compression of Dynamic

Range

Saturn Image

Mag. Spectrum

Mag. Spectrum

in log scale

21/11/15

Duong Anh Duc - Digital Image Processing

13

Compression of Dynamic

Range

Graylevel Slicing: Highlight a specific

range of grayvalues.

21/11/15

Duong Anh Duc - Digital Image Processing

14

Compression of Dynamic

Range

Example:

Highlighted Image (no background)

Original Image

Highlighted Image (with background)

21/11/15

Duong Anh Duc - Digital Image Processing

15

Compression of Dynamic

Range

Bitplane Slicing: Display the different

bits as individual binary images.

21/11/15

Duong Anh Duc - Digital Image Processing

16

Compression of Dynamic

Range

21/11/15

Duong Anh Duc - Digital Image Processing

17

Image Subtraction

In this case, the difference between two

“similar” images is computed to highlight

or enhance the differences between

them:

g(m,n) = f1(m,n)-f2(m,n)

It has applications in image segmentation

and enhancement

21/11/15

Duong Anh Duc - Digital Image Processing

18

Example: Mask mode radiography

f1(m, n): Image before dye injection

f2(m, n): Image after dye injection

21/11/15

g(m, n): Image after dye injection,

followed by subtraction

Duong Anh Duc - Digital Image Processing

19

Image Averaging for noise

reduction

Noise is any random (unpredictable)

phenomenon that contaminates an image.

Noise is inherent in most practical systems:

– Image acquisition

– Image transmission

– Image recording

Noise is typically modeled as an additive process:

g(m,n) = f(m,n) + (m,n)

Noisy

Image

21/11/15

Noise-free

Image

Duong Anh Duc - Digital Image Processing

Noise

20

Image Averaging for noise

reduction

The noise h (m, n) at each pixel (m, n) is modeled

as a random variable.

Usually, h (m, n) has zero-mean and the noise values

at different pixels are uncorrelated.

Suppose we have M observations {gi(m, n)}, i=1, 2, …,

M, we can (partially) mitigate the effect of noise by

“averaging”

g m, n

21/11/15

1

M

M

g i m, n

i 1

Duong Anh Duc - Digital Image Processing

21

Image Averaging for noise

reduction

In this case, we can show that:

E g m, n

Var g m, n

f m, n

1

Var

M

m, n

Therefore, as the number of observations

increases (M

), the effect of noise

tends to zero.

21/11/15

Duong Anh Duc - Digital Image Processing

22

Image Averaging Example

Noise-free Image

21/11/15

Noisy Image

Noise Variance = 0.05

Duong Anh Duc - Digital Image Processing

23

Image Averaging Example

M =2

21/11/15

M =5

Duong Anh Duc - Digital Image Processing

24

Image Averaging Example

M =10

21/11/15

Duong Anh Duc - Digital Image Processing

M =25

25

Image Enhancement

21/11/15

Duong Anh Duc - Digital Image Processing

1

Image Enhancement

To process an image so that output is “visually better”

than the input, for a specific application.

Enhancement is therefore, very much dependent on

the particular problem/image at hand.

Enhancement can be done in either:

– Spatial domain: operate on the original image

g(m,n) = T[f(m,n)]

– Frequency domain: operate on the DFT of the original image

G(u,v) = T[F(u,v)],

where

F(u,v) = F[f(m,n)], and G(u,v) = F [g(m,n)],

21/11/15

Duong Anh Duc - Digital Image Processing

2

Image Enhancement Techniques

Point Operations

•

•

•

•

•

•

•

Image Negative

Contrast

Stretching

Compression of

dynamic range

Graylevel slicing

Image

Subtraction

Image Averaging

Histogram

operations

21/11/15

Mask Operations

•

•

•

•

•

Smoothing

operations

Median Filtering

Sharpening

operations

Derivative

operations

Histogram

operations

Transform Operations

Coloring Operations

•

•

•

•

•

•

•

Low pass

Filtering

Hi pass Filtering

Band pass

Filtering

Homomorphic

Filtering

Histogram

operations

Duong Anh Duc - Digital Image Processing

False Coloring

Full color

Processing

3

Point Operations

Output pixel value g(m, n) at pixel (m, n) depends only on the input

pixel value at f(m, n) at (m, n) (and not on the neighboring pixel

values).

We normally write s = T(r), where s is the output pixel value and r is the

input pixel value.

T is any increasing function that maps [0,1] into [0,1].

21/11/15

Duong Anh Duc - Digital Image Processing

4

Image Negative

T(r) = s = L-1-r, L: max grayvalue

21/11/15

Duong Anh Duc - Digital Image Processing

5

Negative Image

21/11/15

Duong Anh Duc - Digital Image Processing

6

Contrast Stretching

Increase the dynamic range of grayvalues in the input image.

Suppose you are interested in stretching the input intensity values in the

interval [r1, r2]:

Note that (r1- r2) < (s1- s2). The grayvalues in the range [r1, r2] is stretched into

the range [s1, s2].

21/11/15

Duong Anh Duc - Digital Image Processing

7

Contrast Stretching

Special cases:

– Thresholding or

binarization

r1 = r2 , s1 = 0 and s2 = 1

– Useful when we are

only interested in the

shape of the objects

and on on their actual

grayvalues.

21/11/15

Duong Anh Duc - Digital Image Processing

8

Contrast Stretching

21/11/15

Duong Anh Duc - Digital Image Processing

9

Contrast Stretching

Special cases (cont.):

– Gamma correction:

S1 = 0, S2 = 1 and

0, r

g

T r

21/11/15

r1

r r1

, r1 r

r2 r1

1, r r2

r2

Duong Anh Duc - Digital Image Processing

10

Contrast Stretching

Gamma correction

21/11/15

Duong Anh Duc - Digital Image Processing

11

Compression of Dynamic

Range

When the dynamic range of the input

grayvalues is large compared to that of

the display, we need to “compress” the

grayvalue range --- example: Fourier

transform magnitude.

Typically we use a log scale.

s = T(r) = c log(1+ r )

21/11/15

Duong Anh Duc - Digital Image Processing

12

Compression of Dynamic

Range

Saturn Image

Mag. Spectrum

Mag. Spectrum

in log scale

21/11/15

Duong Anh Duc - Digital Image Processing

13

Compression of Dynamic

Range

Graylevel Slicing: Highlight a specific

range of grayvalues.

21/11/15

Duong Anh Duc - Digital Image Processing

14

Compression of Dynamic

Range

Example:

Highlighted Image (no background)

Original Image

Highlighted Image (with background)

21/11/15

Duong Anh Duc - Digital Image Processing

15

Compression of Dynamic

Range

Bitplane Slicing: Display the different

bits as individual binary images.

21/11/15

Duong Anh Duc - Digital Image Processing

16

Compression of Dynamic

Range

21/11/15

Duong Anh Duc - Digital Image Processing

17

Image Subtraction

In this case, the difference between two

“similar” images is computed to highlight

or enhance the differences between

them:

g(m,n) = f1(m,n)-f2(m,n)

It has applications in image segmentation

and enhancement

21/11/15

Duong Anh Duc - Digital Image Processing

18

Example: Mask mode radiography

f1(m, n): Image before dye injection

f2(m, n): Image after dye injection

21/11/15

g(m, n): Image after dye injection,

followed by subtraction

Duong Anh Duc - Digital Image Processing

19

Image Averaging for noise

reduction

Noise is any random (unpredictable)

phenomenon that contaminates an image.

Noise is inherent in most practical systems:

– Image acquisition

– Image transmission

– Image recording

Noise is typically modeled as an additive process:

g(m,n) = f(m,n) + (m,n)

Noisy

Image

21/11/15

Noise-free

Image

Duong Anh Duc - Digital Image Processing

Noise

20

Image Averaging for noise

reduction

The noise h (m, n) at each pixel (m, n) is modeled

as a random variable.

Usually, h (m, n) has zero-mean and the noise values

at different pixels are uncorrelated.

Suppose we have M observations {gi(m, n)}, i=1, 2, …,

M, we can (partially) mitigate the effect of noise by

“averaging”

g m, n

21/11/15

1

M

M

g i m, n

i 1

Duong Anh Duc - Digital Image Processing

21

Image Averaging for noise

reduction

In this case, we can show that:

E g m, n

Var g m, n

f m, n

1

Var

M

m, n

Therefore, as the number of observations

increases (M

), the effect of noise

tends to zero.

21/11/15

Duong Anh Duc - Digital Image Processing

22

Image Averaging Example

Noise-free Image

21/11/15

Noisy Image

Noise Variance = 0.05

Duong Anh Duc - Digital Image Processing

23

Image Averaging Example

M =2

21/11/15

M =5

Duong Anh Duc - Digital Image Processing

24

Image Averaging Example

M =10

21/11/15

Duong Anh Duc - Digital Image Processing

M =25

25

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