Advertisement

InECCE2019 pp 401-413 | Cite as

The Multifocus Images Fusion Based on a Generative Gradient Map

  • Ismail
  • Kamarul Hawari Bin Ghazali
Conference paper
  • 20 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 632)

Abstract

The limitation of camera lens is inability to make focus region for whole scene in one shot image. The camera creates one focus object for one image. It is needed several images to get many focus objects of the scene. It makes difficult to read many focus objects from several images. Multifocus image fusion is a process of combining many focus objects from several images into one image. This operation gives easier way to read focus information from many images clearer. It commonly needed in medical examination, robotics and bioinformatics fields. The clearness information enables machine, computer and human understand the image better and prevents any mistake. In an image, the clear object is only located in focus region. In order to generate all objects in focus region, the multi focus images will be fused into fused image. The methods generally use complicated mathematic equation and hard algorithm. In addition to handle the problem, we design a simple way and have accurate output. Our method is the multifocus image fusion based on generative gradient map. By generative gradient map, it quickly determines the initial prediction of focus region precisely. The Generative gradient map is the external information, generated from gradient of blurred random number image. This procedure substitutes complicated mathematical equations or hard algorithm sequence implementation. Finally, our algorithm able to produces a fused image with high quality. The assessment of our method is according to Mutual Information and Structure Similarity parameter.

Keywords

Multifocus image fusion Generative gradient map Simple method 

References

  1. 1.
    Mishra D (2015) Image fusion techniques: a review. Int J Comput Appl 130(9):7–13Google Scholar
  2. 2.
    Masood S (2017) Image fusion methods: a survey. J Eng Sci Technol Rev 10(6):186–194CrossRefGoogle Scholar
  3. 3.
    Li M (2006) A region based multi-sensor image fusion scheme using pulse coupled neural network. Pattern Recogn Lett 27:1948–1956CrossRefGoogle Scholar
  4. 4.
    Abhyankar M, Khaparde A (2016) Spatial domain decision based image fusion using superimposition. In: Uehara K, Nakamura M (eds) 15th international conference on computer and information science (ICIS), IEEE/ACIS. IEEE, Okayama, pp 247–252Google Scholar
  5. 5.
    Shah P, Kumar A (2012) Multifocus image fusion algorithm using iterative segmentation based on edge information and adaptive threshold. In: Yang R, Chee Lai H (eds) 15th international conference on information fusion (FUSION). IEEE, Singapore, pp 1976–1981Google Scholar
  6. 6.
    Santiago T (2018) Region-based multifocus image fusion for the precise acquisition of pap smear images. J Biomed Opt 23(5):1–9Google Scholar
  7. 7.
    Bavirisetti DP (2018) Multi-focus image fusion using multi-scale image decomposition and saliency detection. Ain Shams Eng J 9:1103–1117CrossRefGoogle Scholar
  8. 8.
    Farid MS (2019) Multi-focus image fusion using content adaptive blurring. Inf Fusion 45:96–112CrossRefGoogle Scholar
  9. 9.
    Pajares G (2004) A wavelet-based image fusion tutorial. Pattern Recogn 37:1855–1872CrossRefGoogle Scholar
  10. 10.
    Yang Y (2014) Multi-focus image fusion using an effective discrete wavelet transform based algorithm. Measurem Sci Rev 14(2):102–108CrossRefGoogle Scholar
  11. 11.
    Jiang Q (2017) A novel multi-focus image fusion method based on stationary wavelet transform and local features of fuzzy sets. IEEE Access 5:20286–20302CrossRefGoogle Scholar
  12. 12.
    Yang Y (2017) A hybrid method for multi-focus image fusion based on fast discrete curvelet transform. IEEE Access 5:14898–14913CrossRefGoogle Scholar
  13. 13.
    Li J (2019) Multifocus image fusion using wavelet-domain-based deep CNN, research article. Comput Intell Neurosci 2019:1–23Google Scholar
  14. 14.
    Salhab O (2018) Survey paper: pseudo random number generators and security tests. J Theor Appl Inf Technol 96(7):1951–1970Google Scholar
  15. 15.
    Paris S (2009) A fast approximation of the bilateral filter using a signal processing approach. Int J Comput Vision 81(1):24–36CrossRefGoogle Scholar
  16. 16.
    Rani S (2014) Detection of edges using mathematical morphological operators. Open Trans Inf Process 1(1):17–26CrossRefGoogle Scholar
  17. 17.
    Kaiming H (2013) Guided image filtering. IEEE Trans PAMI 35(6):1397–1409Google Scholar
  18. 18.
    Hossny M (2008) Comments on ‘information measure for performance of image fusion. Electr Lett 44(18):1066–1067CrossRefGoogle Scholar
  19. 19.
    Wang Z (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ismail
    • 1
    • 2
  • Kamarul Hawari Bin Ghazali
    • 1
  1. 1.Universiti Malaysia PahangGambangMalaysia
  2. 2.Politeknik Negeri PadangPadangIndonesia

Personalised recommendations