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Edge Preserving Image Enhancement via Harmony Search Algorithm

Edge Preserving Image Enhancement via Harmony Search Algorithm. By Zaid Abdi Alkareem Yahya Ibrahim Venkat Mohammed Azmi Al- Betar Ahamad Tajudin Khader. Outline. Background: Image Enhancement Histogram Equalization Harmony Search Algorithm Methodology : Modeling the problem

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Edge Preserving Image Enhancement via Harmony Search Algorithm

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  1. Edge Preserving Image Enhancement via Harmony Search Algorithm By Zaid Abdi Alkareem Yahya Ibrahim Venkat Mohammed Azmi Al-Betar Ahamad Tajudin Khader

  2. Outline • Background: • Image Enhancement • Histogram Equalization • Harmony Search Algorithm • Methodology : • Modeling the problem • Steps of Harmony Search Algorithm • Evaluation steps : • Parameters setting • Dataset used • Experiment result and analysis • CONCLUSION AND FUTURE WORK • Questions & Answer

  3. Image Enhancement • It is a special procedure of processing of an image to produce output image is more suitable for a special applications . Contrast Adjustment Original image image with noise image without noise

  4. Objectives of Image Enhancement • Improving the quality of the images to be more visible to viewers. • Providing better input for another application

  5. Image Enhancement categories Image Enhancement Spatial domain Frequency domain

  6. 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

  7. Histogram Equalization Example Original image enhanced image Histogram of Original image Histogram of the enhanced image

  8. 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 .

  9. Harmony Search Algorithm Harmony Search Analogy

  10. Harmony Search Algorithm Fig1: Analogy between music improvisation and optimization process

  11. Harmony Search Algorithm Fig 2: The harmony memory structure

  12. Harmony Search Flowchart Step 4 Yes Update HM Batter? Step 1 No Initialize Problem and HS parameters Step 3 Stop? Step 2 No Improvise New Harmony Initialize HM Yes Step 5 End

  13. Methodology

  14. The Objective function of modeling IE via HSA g(i,j) = T[f(i,j)] (1)

  15. 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.

  16. 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

  17. HARMONY SEARCH ALGORITHM STEPS Step 2 : Initialize the harmony memory

  18. HARMONY SEARCH ALGORITHM STEPS 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)

  19. HARMONY SEARCH ALGORITHM STEPS Step 4: Update the harmony memory Step 5: Check the stop criterion

  20. Flow chart of the proposed IE model

  21. Evaluation Steps

  22. Parameters setting We have used the maximum number of iterations NI = 200and NVAR=4; %number of variables a, b, c, k HMS = 100 and HMCR=0.9 % harmony consideration rate 0< HMCR <1 PAR = 0.6

  23. Dataset Circuit board, Microscopic view of a tissue segment, A tire And some rice grains. We have implemented the proposed image enhancement algorithm using the MATLAB programming environment.

  24. Experiment result

  25. Experiment result

  26. Experiment and analysis

  27. 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.

  28. Question & Answer Thank You

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