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

2. Background: 1.Image Enhancement 2.Histogram Equalization 3.Harmony Search Algorithm Methodology : 1.Modeling the problem 2.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 2

3. 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 3

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

5. Image Enhancement categories Image Enhancement Spatial domain Frequency domain 5

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 6

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

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. 8

9. Harmony Search Algorithm Musical terms Optimization terms ImprovisationGeneration or construction HarmonySolution vector MusicianDecision variable PitchValue Pitch rangeValue range Audio-aesthetic standardObjective function PracticeIteration Pleasing harmony(Near) – optimal solution Harmony Search Analogy 9

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

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

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

13. Methodology 13

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

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. 15

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 16

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

18. 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 18

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

20. Flow chart of the proposed IE model 20

21. Evaluation Steps 21

22. 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 22

23. 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. 23

24. Experiment result 24

25. Experiment result ImageOriginalHist Eq.HSA Number of edgels Circuit7375 8141 Tissue468647374816 Tire315836933999 Rice954959797277 25

26. Experiment and analysis Image NameEnhance rate Circuit10% Tissue3% Tire27 % Rice- 23% 26

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. 27

28. Thank You Question & Answer 28