Multi-Focus Image Fusion Using Energy Valley Optimization Algorithm
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...
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| Format: | Article |
| Language: | English |
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Çanakkale Onsekiz Mart University
2024-09-01
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| Series: | Journal of Advanced Research in Natural and Applied Sciences |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/3982007 |
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| author | Harun Akbulut |
| author_facet | Harun Akbulut |
| author_sort | Harun Akbulut |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-59747a0611da45179942a4024a2b12d4 |
| institution | Kabale University |
| issn | 2757-5195 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Çanakkale Onsekiz Mart University |
| record_format | Article |
| series | Journal of Advanced Research in Natural and Applied Sciences |
| spelling | doaj-art-59747a0611da45179942a4024a2b12d42025-02-05T18:13:03ZengÇanakkale Onsekiz Mart UniversityJournal of Advanced Research in Natural and Applied Sciences2757-51952024-09-0110366968310.28979/jarnas.1495889453Multi-Focus Image Fusion Using Energy Valley Optimization AlgorithmHarun Akbulut0https://orcid.org/0000-0002-9117-8407NEVSEHIR HACI BEKTAS VELI UNIVERSITYWhen 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.https://dergipark.org.tr/en/download/article-file/3982007multi-focus image fusionenergy valley optimizerblock-based image fusioncomparison of meta-heuristic algorithm |
| spellingShingle | Harun Akbulut Multi-Focus Image Fusion Using Energy Valley Optimization Algorithm Journal of Advanced Research in Natural and Applied Sciences multi-focus image fusion energy valley optimizer block-based image fusion comparison of meta-heuristic algorithm |
| title | Multi-Focus Image Fusion Using Energy Valley Optimization Algorithm |
| title_full | Multi-Focus Image Fusion Using Energy Valley Optimization Algorithm |
| title_fullStr | Multi-Focus Image Fusion Using Energy Valley Optimization Algorithm |
| title_full_unstemmed | Multi-Focus Image Fusion Using Energy Valley Optimization Algorithm |
| title_short | Multi-Focus Image Fusion Using Energy Valley Optimization Algorithm |
| title_sort | multi focus image fusion using energy valley optimization algorithm |
| topic | multi-focus image fusion energy valley optimizer block-based image fusion comparison of meta-heuristic algorithm |
| url | https://dergipark.org.tr/en/download/article-file/3982007 |
| work_keys_str_mv | AT harunakbulut multifocusimagefusionusingenergyvalleyoptimizationalgorithm |