Multi Focus Image Fusion by Differential Evolution Algorithm

Veysel Aslantas, Ahmet Nusret Toprak

2014

Abstract

In applications of imaging system one of the major problems is the limited depth of field which disallow to obtain all-in-focus image. However, microscopic and photographic applications desire to have all-in-focus images. One of the most popular ways to obtain all-in-focus images is the multi focus image fusion. In this paper a novel spatial domain multi focus image fusion method is presented. The method, firstly, calculates point spread functions of the source images by using a technique based on differential evolution algorithm. Then the fused image is constructed by using these point spread functions. Furthermore, the proposed method and other well-known methods are compared in terms of quantitative and visual evaluation.

References

  1. Aslantas, V. & Kurban, R. 2010. Fusion Of Multi-Focus Images Using Differential Evolution Algorithm. Expert System Applications, 37, 8861-8870.
  2. Aslantas, V. & Pham, D. T. 2007. Depth From Automatic Defocusing. Optics Express, 15, 1011-1023.
  3. Burt, P. J. & Kolczynski, R. J. 1993. Enhanced Image Capture Through Fusion. In: Computer Vision, 1993. Proceedings., Fourth International Conference On, 11- 14 May 1993 1993. 173-182.
  4. Das, S. & Suganthan, P. N. 2011. Differential Evolution: A Survey Of The State-Of-The-Art. Evolutionary Computation, Ieee Transactions On, 15, 4-31.
  5. Haghighat, M. B. A., Aghagolzadeh, A. & Seyedarabi, H. 2011. Multi-Focus Image Fusion For Visual Sensor Networks In Dct Domain. Computers & Electrical Engineering, 37, 789-797.
  6. Li, S., Kang, X., Hu, J. & Yang, B. 2013. Image Matting For Fusion Of Multi-Focus Images In Dynamic Scenes. Information Fusion, 14, 147-162.
  7. Li, S., Kwok, J. T. & Wang, Y. 2001. Combination Of Images With Diverse Focuses Using The Spatial Frequency. Information Fusion, 2, 169-176.
  8. Li, S. & Yang, B. 2008. Multifocus Image Fusion Using Region Segmentation And Spatial Frequency. Image And Vision Computing, 26, 971-979.
  9. Pajares, G. & De La Cruz, J. M. 2004. A Wavelet-Based Image Fusion Tutorial. Pattern Recognition, 37, 1855- 1872.
  10. Stathaki, T. 2008. Image Fusion: Algorithms And Applications, Academic Press.
  11. Storn, R. & Price, K. 1997. Differential Evolution - A Simple And Efficient Heuristic For Global Optimization Over Continuous Spaces. Journal Of Global Optimization, 11, 341-359.
  12. Subbarao, M., Wei, T. C. & Surya, G. 1995. Focused Image Recovery From Two Defocused Images Recorded With Different Camera Settings. Image Processing, Ieee Transactions On, 4, 1613-1628.
  13. Xydeas, C. S. & Petrovic, V. 2000. Objective Image Fusion Performance Measure. Electronics Letters, 36, 308-309.
Download


Paper Citation


in Harvard Style

Aslantas V. and Nusret Toprak A. (2014). Multi Focus Image Fusion by Differential Evolution Algorithm . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-039-0, pages 312-317. DOI: 10.5220/0005061103120317


in Bibtex Style

@conference{icinco14,
author={Veysel Aslantas and Ahmet Nusret Toprak},
title={Multi Focus Image Fusion by Differential Evolution Algorithm},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2014},
pages={312-317},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005061103120317},
isbn={978-989-758-039-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Multi Focus Image Fusion by Differential Evolution Algorithm
SN - 978-989-758-039-0
AU - Aslantas V.
AU - Nusret Toprak A.
PY - 2014
SP - 312
EP - 317
DO - 10.5220/0005061103120317