Edge Preserving Image Enhancement via Harmony Search Algorithm

ByZaid Abdi Alkareem YahyaIbrahim VenkatMohammed Azmi Al-Betar Ahamad Tajudin Khader

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• Background:

1. Image Enhancement

2. Histogram Equalization

3. Harmony Search Algorithm

• Methodology :1. Modeling the problem2. Steps of Harmony Search Algorithm

• Evaluation steps :1. Parameters setting 2. Dataset used 3. Experiment result and analysis

• CONCLUSION AND FUTURE WORK

• Questions & Answer

Outline

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• It is a special procedure of processing of an image to produce output image is more suitable for a special applications .

Image Enhancement

Contrast Adjustment

Original image image with noise image without noise

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• Improving the quality of the images to be more

visible to viewers.

• Providing better input for another application

Objectives of Image Enhancement

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Image Enhancement categories

Image Enhancement

Image Enhancement

Spatial domain Spatial domain Frequency domain Frequency domain

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Histogram Equalization

HE is a method to enhance global contrast of an image by using the image ‘s histogram

HE is useful in images with backgrounds and foregrounds that are both bright or both dark

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Histogram Equalization Example

Original image enhanced image

Histogram of Original image Histogram of the enhanced image 7

Harmony Search Algorithm

HSA refers to a new metaheuristic algorithm. Invented in 2001 by Zong Woo Geem . It has dominance and advantages in many applications since its appearance .

Such as real-world applications, Computer science problems, Civil engineering problems And bio & medical applications .

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Harmony Search Algorithm

Musical terms Optimization terms

Improvisation Generation or construction

Harmony Solution vector

Musician Decision variable

Pitch Value

Pitch range Value range

Audio-aesthetic standard Objective function

Practice Iteration

Pleasing harmony (Near) – optimal solution

Harmony Search Analogy 9

Harmony Search Algorithm

Fig1: Analogy between music improvisation and optimization process

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Harmony Search Algorithm

Fig 2: The harmony memory structure

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Harmony Search Flowchart

Step 4

Step 5

Initialize Problem and HS parameters Initialize Problem

and HS parameters

Initialize HMInitialize HM Stop?Stop?

Batter?Batter?

Improvise New Harmony

Improvise New Harmony

Update HMUpdate HM

End End

Step 1

Step 2 Step 3

No

No

Yes

Yes

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Methodology

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The Objective function of modeling IE via HSA

g(i,j) = T[f(i,j)] (1)

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The objectives HSA in Image enhancement

• Increasing the relative number of edges in the

image

• Enhance the overall intensity of edges

• Improve the entropy measure in the image.

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HARMONY SEARCH ALGORITHM STEPS

Step 1 : Initialize Problem and max {f (x)|x X} ∈ HSA parameters : HMCR : Harmony Memory Consideration Rate HMS : Harmony Memory Size PAR : Pitch Adjustment Rate NI : Number of Improvisations

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Step 2 : Initialize the harmony memory

HARMONY SEARCH ALGORITHM STEPS

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Step 3 : Improvise a new harmony In this step, the HSA will generate (improvise) a new

harmony vector from scratch x = (a, b, c, k)

HARMONY SEARCH ALGORITHM STEPS

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Step 4: Update the harmony memory

Step 5: Check the stop criterion

HARMONY SEARCH ALGORITHM STEPS

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Flow chart of the proposed IE model

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Evaluation Steps

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Parameters setting

We have used the maximum number of iterations

NI = 200 and

NVAR=4; %number of variables a, b, c, k

HMS = 100 and

HMCR=0.9 % harmony consideration rate 0< HMCR <1

PAR = 0.6

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Dataset

We have implemented the proposed image

enhancement algorithm using the MATLAB

programming environment.

Circuit board,

Microscopic view of a tissue segment,

A tire

And some rice grains.

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Experiment result

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Experiment result

Image Original Hist Eq. HSA

Number of edgels

Circuit 7375 7375 8141

Tissue 4686 4737 4816

Tire 3158 3693 3999

Rice 9549 5979 7277

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Experiment and analysis

Image Name Enhance rate

Circuit 10%

Tissue 3%

Tire 27 %

Rice - 23%

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CONCLUSION AND FUTURE WORK

• HSA to enhance the images by preserving the edges.

• Using standard Dataset. • We have compared our approach with (HE).

• Our approach shows result better than HE algorithm.

• In the near future we would like to explore more on the behavioral aspect of the HSA with respect to more advanced image processing algorithms.

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Thank You

Question & Answer

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