Advertisement

Research on Multi-focus Image Fusion Algorithm Based on Quadtree

  • Senlin Wang
  • Junhai Zhou
  • Qin Liu
  • Zheng Qin
  • Panlin Hou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11342)

Abstract

Most of the multi-focus fusion algorithms currently are prone to image blur and loss of detail information. This paper proposes a multi-focus fusion algorithm based on quadtree decomposition which can almost overcome the above shortcomings. The research on multi-focus fusion algorithm based on quadtree decomposition is to divide the original image into several image sub-blocks and check regional consistency for each block to obtain the optimal block of the source image, and then detect the focus area for each block to obtain the initial fused decision image. Finally, the fused decision image is subjected to perform morphological processing to obtain a final fused image. Through extensive experiments on different source images, we show that the proposed method has better adaptability.

Keywords

Quadtree Optimal block Regional consistency 

Notes

Acknowledgements

This work is partially supported by the National Science Foundation of China under Grant Nos. 61872133, 61472131, 61772191.

References

  1. 1.
    Liu, Y., Chen, X., Ward, R.K.: Image fusion with convolutional sparse representation. IEEE Signal Process. Lett. 23(12), 1882–1886 (2016)CrossRefGoogle Scholar
  2. 2.
    Zhang, Y., Chen, L., Zhao, Z.: Multi-focus image fusion based on robust principal component analysis and pulse-coupled neural network. Int. J. Light Electron Opt. 125(17), 5002–5006 (2014)CrossRefGoogle Scholar
  3. 3.
    Zhang, X., Li, X., Feng, Y.: Image fusion based on simultaneous empirical wavelet transform. Multimed. Tools Appl. 76(6), 1–19 (2017)Google Scholar
  4. 4.
    Kurian, A.P., Bijitha, S.R., Mohan, L.: Performance evaluation of modified SVD based image fusion. Int. J. Comput. Appl. 58(12), 38 (2012)Google Scholar
  5. 5.
    Cai, M., Yang, J., Cai, G.: Multi-focus image fusion algorithm using LP transformation and PCNN. In: 6th IEEE International Conference on Software Engineering and Service Science, pp. 237–241. IEEE, Beijing (2015)Google Scholar
  6. 6.
    Ding, S., Zhao, X., Xu, H.: NSCT-PCNN image fusion based on image gradient motivation. IET Comput. Vis. 12(4), 377–383 (2018)CrossRefGoogle Scholar
  7. 7.
    Bai, B., Li, F., Shen, Q.: Image fusion via nonlocal sparse K-SVD dictionary learning. Appl. Opt. 55(7), 1814 (2016)CrossRefGoogle Scholar
  8. 8.
    Xu, X., Wang, Y., Chen, S.: Medical image fusion using discrete fractional wavelet transform. Biomed. Signal Process. Control 27(3), 103–111 (2016)CrossRefGoogle Scholar
  9. 9.
    Liu, Z., Li, X., Luo, P.: Semantic image segmentation via deep parsing network. In: 2015 IEEE International Conference on Computer Vision, pp. 1377–1385. IEEE, Santiago (2015)Google Scholar
  10. 10.
    Lee, K., Ji, S.: Multi-focus image fusion using energy of image gradient and gradual boundary smoothing. In: TENCON 2015–2015 IEEE Region 10 Conference, pp. 1–4. IEEE, Macao (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Senlin Wang
    • 1
  • Junhai Zhou
    • 1
  • Qin Liu
    • 1
  • Zheng Qin
    • 1
  • Panlin Hou
    • 1
  1. 1.Hunan UniversityChagnshaChina

Personalised recommendations