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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Journal of Advanced Research in Natural and Applied Sciences Ročník 10; číslo 3; s. 669 - 683
Hlavní autor:	Akbulut, Harun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Çanakkale Onsekiz Mart University 30.09.2024
Témata:	block-based image fusion comparison of meta-heuristic algorithm energy valley optimizer multi-focus image fusion
ISSN:	2757-5195, 2757-5195
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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.
ISSN:	2757-5195 2757-5195
DOI:	10.28979/jarnas.1495889