CH03 Image+Enhancement+in+the+Spatial+Domain

Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.

1 중앙대학교 첨단영상대학원

CH 3 Image Enhancement in the Spatial DomainCH 3 Image Enhancement in the Spatial Domain

3.1 Background3.1 Background

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image processed the:),(

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Point ProcessingPoint Processing

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3.2 Gray Level Transformations3.2 Gray Level Transformations



3.2.1 Image Negatives3.2.1 Image Negatives

rLs 1



3.2.2 Log Transformations3.2.2 Log Transformations

rcs 1log



3.2.3 Power-Law Transformation3.2.3 Power-Law Transformation

crs



Gamma CorrectionGamma Correction



Gamma Correction: Example 3.1Gamma Correction: Example 3.1



Gamma Correction: Example 3.2Gamma Correction: Example 3.2



3.2.4 Piecewise-Linear Transformation

Contrast Stretching

3.2.4 Piecewise-Linear Transformation

Contrast Stretching



Gray-Level SlicingGray-Level Slicing



Bit-Plane SlicingBit-Plane Slicing



3.3 Histogram Processing

What is Histogram?

3.3 Histogram Processing

What is Histogram?

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levelgray having image in the pixels ofnumber :

levelgray th - :

Histogram

Lk

rn

kr

nrh

kk

k

kk

1,,1,0for

levelgray having image in the pixels ofnumber :

levelgray th - :

image in the pixels ofnumber total:

Histogram Normalized

Lk

rn

kr

n

nnrp

kk

k

kk



3.3.1 Histogram Equalization

Fundamental Assumptions on the Transformation

3.3.1 Histogram Equalization

Fundamental Assumptions on the Transformation

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increasinglly monotonica valued,-single i

:on sassumption Two

10 ,

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Histogram Equalization

The Continuous Case


The Continuous Case

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y theory,probabilit elementaryan From

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The Discrete Case


The Discrete Case

1,...,1,0 ,

1,...,1,0 ,

00

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m

j

jk

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3.3.2 Histogram Matching (Specification)

Development of Method

3.3.2 Histogram Matching (Specification)

Development of Method



Histogram Matching (Specification)

Implementation

Histogram Matching (Specification)

Implementation



Chapter 3Chapter 3



3.3.3 Local Enhancement3.3.3 Local Enhancement



3.3.4 Use of Histogram Statisticsfor Image Enhancement

3.3.4 Use of Histogram Statisticsfor Image Enhancement

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otherwise,

and if,, 2100

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3.4 Enhancement UsingArithmetic/Logic Operations

3.4 Enhancement UsingArithmetic/Logic Operations



3.4.1 Image Subtraction3.4.1 Image Subtraction



Image Subtraction: Mask Mode RadiographyImage Subtraction: Mask Mode Radiography



3.4.2 Image Averaging3.4.2 Image Averaging

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yxyx

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3.5 Basics of Spatial Filtering3.5 Basics of Spatial Filtering

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,,,

1,11,1,10,1,0,0

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mn

iiimnmn zwzwzwR

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3.6 Smoothing Spatial Filters

3.6.1 Smoothing Linear Filters

3.6 Smoothing Spatial Filters

3.6.1 Smoothing Linear Filters

a

as

b

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a

as

b

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Averaging Filter: Hubble ImageAveraging Filter: Hubble Image



3.6.2 Order Statistics Filters3.6.2 Order Statistics Filters



3.7 Sharpening Spatial Filters

3.7.1 Foundation

3.7 Sharpening Spatial Filters

3.7.1 Foundation

121

derivativeorder -second 1D, The

1

derivativeorder -first 1D, The

2

2

xfxfxfx

f

xfxfx

f



3.7.2 Second Derivatives: Laplacian3.7.2 Second Derivatives: Laplacian

),(4

)1,()1,(),1(),1(

)1,(),(2)1,(

),1(),(2),1(

Laplacian :derivativeorder -second 2D, The

2

2

2

2

2

2

2

2

22

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yxfyxfyxfy

f

yxfyxfyxfx

f

x

f

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ff



LaplacianLaplacian

positive iscenter theif),(),(

negative iscenter theif),(),(),(

2

2

yxfyxf

yxfyxfyxg



Laplacian Enhancement: SimplificationLaplacian Enhancement: Simplification

)]1,()1,(

),1(),1([),(5

),(),(),( 2

yxfyxf

yxfyxfyxf

yxfyxfyxg



Unsharp Masking and High-Boost FilteringUnsharp Masking and High-Boost Filtering

),(,,

filteringboost -High

),(,,

masking Unsharp

yxfyxAfyxf

yxfyxfyxf

hb

s



3.7.3 First Derivatives: The Gradient3.7.3 First Derivatives: The Gradient



3.8 Combining Spatial Enhancement Methods3.8 Combining Spatial Enhancement Methods



Homework #2• Problems in Chapter 3

– 3.2(a), 3.8, 3.22, 3.27

CH03 Image+Enhancement+in+the+Spatial+Domain

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