Multi-Focus Image Fusion Using Energy Valley Optimization Algorithm

Abstract

When a natural scene is photographed using imaging sensors commonly used today, part of the image is obtained sharply while the other part is obtained blurry. This problem is called limited depth of field. This problem can be solved by fusing the sharper parts of multi-focus images of the same scene. These methods are called multi-focus image fusion methods. This study proposes a block-based multi-focus image fusion method using the Energy Valley Optimization Algorithm (EVOA), which has been introduced in recent years. In the proposed method, the source images are first divided into uniform blocks, and then the sharper blocks are determined using the criterion function. By fusing these blocks, a fused image is obtained. EVOA is used to optimize the block size. The function that maximizes the quality of the fused image is used as the fitness function of the EVOA. The proposed method has been applied to commonly used image sets. The obtained experimental results are compared with the well-known Genetic Algorithm (GA), Differential Evolution Algorithm (DE), and Artificial Bee Colony Optimization Algorithm (ABC). The experimental results show that EVOA can compete with the other block-based multi-focus image fusion algorithms.

Keywords

• Multi-focus image fusion
• energy valley optimizer
• block-based image fusion
• comparison of meta-heuristic algorithm

References

1. V. Aslantas, R. Kurban, Fusion of multi-focus images using differential evolution algorithm, Expert Systems with Applications 37 (12) (2010) 8861–8870.
2. C. Akyel, Diagnosis of oral cancer from histopathological images with xception, Journal of Advanced Research in Natural and Applied Sciences 9 (2) (2023) 283–290.
3. H. Avcı, J. Karakaya, Effect of different parameter values for pre-processing of using mammography images, Journal of Advanced Research in Natural and Applied Sciences 9 (2) (2023) 345–354.
4. F. Çakıroğlu, R. Kurban, A. Durmuş, E. Karaköse, Multi-focus image fusion by using swarm and physics based metaheuristic algorithms: A comparative study with Archimedes, atomic orbital search, equilibrium, particle swarm, artificial bee colony and jellyfish search optimizers, Multimedia Tools and Applications 82 (29) (2023) 44859–44883.
5. G. Pajares, J. M. De La Cruz, A wavelet-based image fusion tutorial, Pattern Recognition 37 (9) (2004) 1855–1872.
6. P. Burt, E. Adelson, The Laplacian Pyramid as a compact image code, IRE Transactions on Communications Systems 31 (4) (1983) 532–540.
7. S. Li, B. Yang, Multifocus image fusion by combining curvelet and wavelet transform, Pattern Recognition Letters 29 (9) (2008) 1295–1301.
8. M. N. Do, M. Vetterli, The contourlet transform: An efficient directional multiresolution image representation, IEEE Transactions on Image Processing 14 (12) (2005) 2091–2106.

9. Q.-G. Miao, C. Shi, P.-F. Xu, M. Yang, Y.-B. Shi, A novel algorithm of image fusion using shearlets, Optics Communications 284 (6) (2011) 1540–1547.
10. M. Nejati, S. Samavi, S. Shirani, Multi-focus image fusion using dictionary-based sparse representation, Information Fusion 25 (2015) 72–84.
11. M. Nejati, S. Samavi, N. Karimi, S. M. R. Soroushmehr, S. Shirani, I. Roosta, K. Najarian, Surface area-based focus criterion for multi-focus image fusion, Information Fusion 36 (2017) 284–295.
12. S. Bhat, D. Koundal, Multi-focus image fusion techniques: A survey, Artificial Intelligence Review 54 (8) (2021) 5735–5787.
13. S. Li, J. T. Kwok, Y. Wang, Combination of images with diverse focuses using the spatial frequency, Information Fusion 2 (3) (2001) 169–176.
14. Y. Chen, J. Guan, W.-K. Cham, Robust Multi-Focus image fusion using Edge model and Multi-Matting, IEEE Transactions on Image Processing 27 (3) (2018) 1526–1541.
15. A. N. Toprak, V. Aslantas, Fusion of multi-focus image by blocks optimal positions, in: E. Adalı, Ş. Sağıroğlu (Eds.), 3rd International Conference on Computer Science and Engineering (UBMK), Sarayova, 2018, pp. 471–476.
16. V. Aslantas, A. N. Toprak, Multi focus image fusion by differential evolution algorithm, in: J. Filipe, O. Gusikhin (Eds), 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Vienna, 2014, pp. 312–317.
17. J. Kong, K. Zheng, J. Zhang, X. Feng, Multi-focus image fusion using spatial frequency and genetic algorithm, International Journal of Computer Science and Network Security 8 (2) (2008) 220–224.
18. R. Özdemir, M. Taşyürek, V. Aslantaş, Improved Marine Predators Algorithm and Extreme Gradient Boosting (XGBoost) for shipment status time prediction, Knowledge-Based Systems 294 (2024) 111775 20 pages.
19. M. Taşyürek, M. Erat, Determining the best meter reading route using ant colony and genetic algorithm methods, Dicle University Journal of Engineering 13 (3) (2022) 405–412.
20. M. Azizi, U. Aickelin, H. A. Khorshidi, M. B. Shishehgarkhaneh, Energy valley optimizer: A novel metaheuristic algorithm for global and engineering optimization, Scientific Reports 13 (1) (2023) Article Number 226 23 pages.
21. M. Eskicioglu, P. S. Fisher, Image quality measures and their performance, IEEE Transactions on Communications 43 (12) (1995) 2959–2965.
22. V. Aslantas, R. Kurban, A comparison of criterion functions for fusion of multi-focus noisy images, Optics Communications 282 (16) (2009) 3231–3242.
23. J. H. Holland, An introductory analysis with applications to biology, control and artificial intelligence, Adaptation in Natural and Artificial System, MIT Press, Cambridge, 1992, Ch. 1-14, 211 pages.
24. R. Storn, K. Price, Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization 11 (4) (1997) 341–359.
25. D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm, Journal of Global Optimization 39 (3) (2007) 459–471.
26. K. Ma, N. K. Zeng, N. Z. Wang, Perceptual quality assessment for multi-exposure image fusion, IEEE Transactions on Image Processing 24 (11) (2015) 3345–3356.
27. Y. Liu, S. Liu, Z. Wang, A general framework for image fusion based on multi-scale transform and sparse representation, Information Fusion 24 (2015) 147–164.
28. C. S. Xydeas, V. Petrovic, Objective image fusion performance measure, Electronics Letters 36 (4) (2000) 308 3 pages.
29. Y. Chen, R. S. Blum, A new automated quality assessment algorithm for image fusion, Image and Vision Computing 27 (10) (2009) 1421–1432.
30. B. Wei, X. Feng, K. Wang, B. Gao, The Multi-Focus-Image-Fusion method based on convolutional neural network and sparse representation, Entropy 23 (7) (2021) 827 16 pages.
31. V. Aslantas, E. Bendes, A new image quality metric for image fusion: The sum of the correlations of differences, AEÜ. International Journal of Electronics and Communications 69 (12) (2015) 1890–1896.