Multi-focus image fusion using best-so-far ABC strategies

Theory and Applications of Soft Computing Methods
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Abstract

Multi-focus image fusion is a process of combining a set of images that have been captured from the same scene but with different focuses in order to construct an additional sharper image. This process plays an important role in the image processing and machine vision fields. Various algorithms have been developed for this task. The key challenge in the design of multi-focus image fusion algorithms is how to evaluate the local content information of each image from the source images. Simple, but effective, block-based techniques at pixel level are widely used for multi-focus image fusion. However, a fixed block size may not be applicable to every application. A block size that is too small or too large is also not desirable. Hence, optimization of the block size is necessary in order to obtain a fused image that comprises the sharper parts of the source images. Recently, a number of techniques based on evolutionary computation have been applied to block-based multi-focus image fusion. The artificial bee colony (ABC) algorithm is one of the more popular evolutionary computational approaches used to find an optimal solution. In this paper, an efficient and robust block-based multi-focus image fusion method based on the optimal selection of sharper image blocks from source images using best-so-far ABC strategies is proposed. Experiment results show that the proposed method is able to provide good results and outperforms other conventional methods, both visually and quantitatively.

Keywords

Block-based multi-focus image fusion Energy of Laplacian (EOL) Sum-modified-Laplacian (SML) Contrast visibility Spatial frequency (SF) Artificial bee colony (ABC) algorithm Best-so-far ABC 

Notes

Acknowledgments

This work is partially supported by the Faculty of Engineering at Si Racha, Kasetsart University Sriracha Campus (2557/2).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This chapter does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© The Natural Computing Applications Forum 2015

Authors and Affiliations

  1. 1.Computational Intelligence Research Laboratory (CIRLab), Computer Engineering Department, Faculty of Engineering at Si RachaKasetsart University Sriracha CampusChonburiThailand

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