Denizanası Arama Optimizasyon Algoritması ile Çok-Odaklı Görüntülerin Birleştirilmesi

Öz

Bir sahnenin görüntüsü çekilirken lens belirli bir mesafede bulunan nesnelere odaklanır ve diğer uzaklıkta bulunan nesneler ise bulanık olur. Buna sınırlı alan derinliği problem adı verilir. Çok-odaklı görüntü birleştirme yöntemi bu problemi çözmek için kullanılan bir yöntemdir. Çok-odaklı görüntü birleştirme yöntemi kullanılarak sahnenin tamamının net görüntüsü elde edilir. Bu yöntem için farklı odaklarda çekilmiş en az iki görüntü birleştirilir. Çok-odaklı görüntü birleştirme için klasik görüntü birleştirme yöntemlerine ek olarak çeşitli algoritmalar geliştirilmiştir. Çok-odaklı görüntü birleştirme için piksel düzeyinde blok tabanlı yöntemler yaygın olarak kullanılır. Kullanılabilecek blok boyutu birleştirme performansını önemli ölçüde etkileyen bir faktördür. Dolayısıyla blok boyutunun optimize edilmesi gerekmektedir. Bu makalede, deniz anası arama (JSA) optimizasyon algoritması kullanılarak kaynak görüntülerden daha net görüntü bloklarının optimal seçimine dayanan, blok tabanlı çok-odaklı görüntü birleştirme yöntemi önerilmiştir. Geleneksel görüntü birleştirme yöntemlerinden olan DWTPCA, DCHWT, APCA, PCA, SWTDWT ve SWT metotları ile metasezgisel yöntemlerden olan yapay arı kolonisi (ABC) ve JSA sonuçları kıyaslanmıştır. Ayrıca JSA metodunun hem görsel hem de nicel olarak karşılaştırıldığında diğer yöntemlerden daha iyi performansa sahip olduğunu belirlenmiştir.

Anahtar Kelimeler

• Görüntü Birleştirme
• Sınırlı alan derinliği artırma
• Deniz anası arama optimizasyon algoritması.

Fusion of Multi-Focus Images using Jellyfish Search Optimizer

Öz

When obtaining an image of a scene, the lens focuses on objects at a certain distance, and objects at other distances are blurred. This is called the limited depth of field problem. An approach for solving this problem is multi-focus image fusion. A clearer view of the entire scene is obtained by using the multi-focus image fusion method. For this method, at least two images captured at different focuses are combined. Various algorithms have been developed for multi-focus image fusion methods. For multi-focus image fusion, pixel-level block-based methods are commonly used. The block size is a factor that significantly affects the fusion performance. As a result, the block size parameter must be improved. The Jellyfish search optimization algorithm (JSA) is used to propose a block-based multi-focus image fusion approach based on the optimal selection of clearer image blocks from source images. The results of DWTPCA, DCHWT, APCA, PCA, SWTDWT and SWT methods, which are traditional image fusion methods, and ABC (artificial bee colony) and JSA optimization algorithms, which are metaheuristic methods, are compared. In addition, it has been determined that the JSA method has better performance than other traditional methods when compared both visually and quantitatively.

Anahtar Kelimeler

• Image Fusion
• Extending depth of field
• Jellyfish Search optimization algorithm.

Kaynakça

1. Goshtasby, A.A. and S.G. Nikolov, Guest editorial: Image fusion: Advances in the state of the art. Information Fusion, 2007. 8(2): p. 114-118.
2. Garg, R., P. Gupta, and H. Kaur. Survey on multi-focus image fusion algorithms. in 2014 Recent Advances in Engineering and Computational Sciences (RAECS). 2014. IEEE.
3. Meher, B., et al., A survey on region based image fusion methods. 2019. 48: p. 119-132.
4. Irshad, H., et al. Image fusion using computational intelligence: A survey. in 2009 Second International Conference on Environmental and Computer Science. 2009. IEEE.
5. Nejati, M., et al., Surface area-based focus criterion for multi-focus image fusion. 2017. 36: p. 284-295.
6. Nejati, M., S. Samavi, and S.J.I.F. Shirani, Multi-focus image fusion using dictionary-based sparse representation. 2015. 25: p. 72-84.
7. Zhang, Y., et al., IFCNN: A general image fusion framework based on convolutional neural network. 2020. 54: p. 99-118.
8. Aslantas, V. and A.N.J.O.C. Toprak, A pixel based multi-focus image fusion method. 2014. 332: p. 350-358.

9. Bai, X., et al., Multi-focus image fusion through gradient-based decision map construction and mathematical morphology. 2016. 4: p. 4749-4760.
10. Aslantas, V. and R.J.E.S.w.A. Kurban, Fusion of multi-focus images using differential evolution algorithm. 2010. 37(12): p. 8861-8870.
11. Agrawal, S., S. Swain, and L. Dora. BFO-ICA based multi focus image fusion. in 2013 IEEE Symposium on Swarm Intelligence (SIS). 2013. IEEE.
12. Phamila, Y.A.V. and R.J.S.P. Amutha, Discrete Cosine Transform based fusion of multi-focus images for visual sensor networks. 2014. 95: p. 161-170.
13. Cao, L., et al., Multi-focus image fusion based on spatial frequency in discrete cosine transform domain. 2014. 22(2): p. 220-224.
14. Vijayarajan, R., S.J.A.-I.J.o.E. Muttan, and Communications, Discrete wavelet transform based principal component averaging fusion for medical images. 2015. 69(6): p. 896-902.
15. Wang, Z., et al., A comparative analysis of image fusion methods. 2005. 43(6): p. 1391-1402.
16. Eskicioglu, A.M. and P.S.J.I.T.o.c. Fisher, Image quality measures and their performance. 1995. 43(12): p. 2959-2965.
17. Li, S., J.T. Kwok, and Y.J.I.f. Wang, Combination of images with diverse focuses using the spatial frequency. 2001. 2(3): p. 169-176.
18. Xydeas, C.a. and V.J.E.l. Petrovic, Objective image fusion performance measure. 2000. 36(4): p. 308-309.
19. Aslantas, V., E.J.A.-i.J.o.e. Bendes, and communications, A new image quality metric for image fusion: The sum of the correlations of differences. 2015. 69(12): p. 1890-1896.
20. Li, S., R. Hong, and X. Wu. A novel similarity based quality metric for image fusion. in 2008 International Conference on Audio, Language and Image Processing. 2008. IEEE.
21. Bastian, T., et al., Ecosystem relevance of variable jellyfish biomass in the Irish Sea between years, regions and water types. 2014. 149: p. 302-312.
22. Dorigo, M., M. Birattari, and T.J.I.c.i.m. Stutzle, Ant colony optimization. 2006. 1(4): p. 28-39.
23. Fossette, S., et al., A biologist’s guide to assessing ocean currents: a review. 2012. 457: p. 285-301.
24. Fossette, S., et al., Current-oriented swimming by jellyfish and its role in bloom maintenance. 2015. 25(3): p. 342-347.
25. Brotz, L., et al., Increasing jellyfish populations: trends in large marine ecosystems, in Jellyfish blooms IV. 2012, Springer. p. 3-20.
26. Dong, Z., D. Liu, and J.K.J.M.p.b. Keesing, Jellyfish blooms in China: dominant species, causes and consequences. 2010. 60(7): p. 954-963.
27. Mariottini, G.L. and L.J.M.d. Pane, Mediterranean jellyfish venoms: A review on scyphomedusae. 2010. 8(4): p. 1122-1152.
28. Zavodnik, D.J.M.B., Spatial aggregations of the swarming jellyfish Pelagia noctiluca (Scyphozoa). 1987. 94(2): p. 265-269.
29. Xu, J. and J. Zhang. Exploration-exploitation tradeoffs in metaheuristics: Survey and analysis. in Proceedings of the 33rd Chinese control conference. 2014. IEEE.
30. Liu, Y., et al., Multi-focus image fusion: A survey of the state of the art. 2020. 64: p. 71-91.