4-image enhancement

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Image Enhancement


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References

1. Gonzalez and Woods, Digital Image

Processing, 2nd Edition, Prentice Hall,

2002.

2. Jahne, Digital Image Processing, 5th

Edition, Springer 2002.

3. Jain, Fundamentals of Digital Image

Processing, Prentice Hall 1989


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Overview

Human perception (focus of this

discussion)

Machine perception (ocr)

Application specific

Heuristic based: result better than the

original image subjective assessment

Spatial vs frequency domain


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Spatial Domain

Based on the collection of pixels in the

image

Enhancement techniques will yield Noise reduction

Neighborhood smoothing

Highlighting of desired feature


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Spatial Domain Math Framework

Typically, spatial domain based enhancement involves:

g(x,y) = T [f(x,y)],where f = input image; g = output image; T = operator defined on f based on a neighborhood

of x,y. If the neighborhood is 1x1 pixel, then the output intensity is dependent on the current

intensity value of the pixel, and can be represented as

s = T( r )

where r and s are gray level values of f(x,y) and g(x,y) at location x,y. In such situations T is

a gray-level transformation function.

From [1]


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Examples of Gray level Transformation

Functions (Point Processing)

Contrast Stretching

Best if input is 0

outside a range ofvalues.

Thresholding:

Result is a

binary Image

From [1]


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Larger Neighborhoods

Objective determine g(x,y) based on

input intensity (gray level) values f(x,y) in

the neighborhood of x,y.

Mask processing or filtering

Each of the elements in the neighborhood has

an associated weight

g(x,y) depends on f(a,b)|a,b N(x,y)


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Basic Gray Level Transformations

Dark

Ligh

t

Dark

Light

From [1]


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Image Negatives

In this example, using the image negative, it is easier toanalyze the breast tissue.

From [1]


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Log transformations

s = c log (1 + r)

r >= 0; hence 1 + r > 0; log 0 = ?

Log transformations are useful, when there is a large dynamic

range for the input variable ( r ).

Range: 0 to 1.5*106 Range: 0 to 6.2

From [1]


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Power law transformation

Stretch higher

(lighter) gray levels

Stretch lower

(ldarker) gray levels

Many display devices (e.g.

CRT) respond like the power

law, i.e intensity voltage

relationship is power law based

gamma 1.8 to 2.5. The display

will tend to produce images

darker than intended. So the

display is distorted. Gamma

correction is used to correct for

this distortion.

From [1]


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Display distortion correction

1. Gamma correction

can also fix the

distortions in color.

2. More important with

the internet.

3. Many viewers, varietyof monitors.

4. Gamma of view

station is not known.

5. Preprocess using an

average gamma.

From [1]


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Power law contrast manipulation

(c) better than (b).

(d) Background is

better than (c) but

washed out effect.

From [1]


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Piece-wise Linear Transformation

Contrast Stretching

Mean gray level value

of the image

From [1]


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Gray level Slicing

From [1]


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Bit plane Slicing

From [1]

MSb (bit 7)

LSb (bit 0)


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Histograms

Histogram - frequency of occurrence of a gray levelvalue

Normalizing histograms with rest to the total number of

pixels converts these into probability density like function

Histogram processing yields robust image processingresults

Histograms are NOT unique


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Histograms for 4 images

From [1]

1. For high contrast, it is best to have a

larger range of gray level values.

2. If we could transform an image with a

resulting change in histogram, then

that may yield more contrast.

3. We need to study the rules fortransforming histograms, and study

the resulting impact on images.


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Histogram Equalization

From [1]


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Transformation Functions

From [1]


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Mapping for Histogram

Specification

From [1]


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Example of Histogram Specification

From [1]


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Continued

From [1]


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Localized Histogram Equalization

From [1]


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From [1]


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Histogram Stats

From [1]

4-image enhancement

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