Skip to main content
SpringerLink
Log in

Multi-focus Image Fusion Using Hybrid De-focused Region Segmentation Approach

  • Research Article-Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Multi-focus image fusion aims to obtain necessary details from multiple source images, having varied level of focus depths, in order to generate an all-in-focus fused image that ideally contains all the information from source images. This paper presented an image matting-based fusion approach to combine focus information of multiple source images based on correlation between nearby pixels. First the focused pixels of source images are identified by perceiving the sharpness of the images by utilizing multiple sharpness metrics. Trimap is generated from focused maps to obtain prior information and image matting is applied to accurately segment the focused and de-focused regions of the source images. In the end, the focused regions from multiple source images are integrated to obtain a well formed and consistent fused image. Experiments and comparison with various existing fusion techniques verify the significance of proposed technique.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Kaur, G.; Kaur, P.: Survey on multi-focus image fusion techniques. In: International Conference on Electrical, Electronics, and Optimization Techniques, pp. 1420–1424 (2016)

  2. Dulhare, U.; Khaled, A. M.; Ali, M. H.: A review on diversified mechanisms for multi focus image fusion. In: International Conference on Communication and Information Processing, pp. 1–6 (2019)

  3. Kou, L., Zhang, L., Zhang, K., Sun, J., Han, Q., Jin, Z.: A multi-focus image fusion method via region mosaicking on Laplacian pyramids. PLoS ONE 13(5), e0191085 (2018)

    Article  Google Scholar 

  4. Vijayarajan, R., Muttan, S.: Discrete wavelet transform based principal component averaging fusion for medical images. AEU Int. J. Electron. Commun. 69(6), 896–902 (2015)

    Article  Google Scholar 

  5. Kalaivani, K., Asnath, Y.: Pixel level fusion of multi temporal landsat images using discrete wavelet transform for detecting changes. J. Adv. Res. Dynamical Control Syst. 9(5), 125–130 (2017)

    Google Scholar 

  6. Liu, Y., Liu, S., Wang, Z.: Multi-focus image fusion with dense SIFT. Inf. Fusion 23, 139–155 (2015)

    Article  Google Scholar 

  7. Wang, J., Peng, J., Feng, X., He, G., Wu, J., Yan, K.: Image fusion with nonsubsampled contourlet transform and sparse representation. J. Electron. Imag. 22(4), 043019 (2013)

    Article  Google Scholar 

  8. Yang, G., Li, M., Chen, L., Yu, J.: The nonsubsampled contourlet transform based statistical medical image fusion using generalized gaussian density. Comput. Math. Methods Med. 2015, 262819 (2015)

    MathSciNet  MATH  Google Scholar 

  9. Li, H., Li, L., Zhang, J.: Multi-focus image fusion based on sparse feature matrix decomposition and morphological filtering. Optics Commun. 342, 1–11 (2015)

    Article  Google Scholar 

  10. Liu, X., Zhou, Y., Wang, J.: Image fusion based on shearlet transform and regional features. AEU Int. J. Electron. Commun. 68(6), 471–477 (2014)

    Article  Google Scholar 

  11. Naji, M. A.; Aghagolzadeh, A.: Multi-focus image fusion in DCT domain based on correlation coefficient. In: International Conference on Knowledge-Based Engineering and Innovation, pp. 632–639 (2015)

  12. Song, Y., Wu, W., Liu, Z., Yang, X., Liu, K., Lu, W.: An adaptive pansharpening method by using weighted least squares filter. IEEE Geosci. Remote Sens. Lett. 13(1), 18–22 (2016)

    Article  Google Scholar 

  13. Jian, L., Yang, X., Zhou, Z., Zhou, K., Liu, K.: Multi-scale image fusion through rolling guidance filter. Future Generat. Comput. Syst. 83(C), 310–325 (2018)

    Article  Google Scholar 

  14. Duana, J., Chen, L., Chen, C.L.P.: Multifocus image fusion using superpixel segmentation and superpixel-based mean filtering. Appl. Opt. 55(36), 10352–10362 (2016)

    Article  Google Scholar 

  15. Duana, J., Chen, L., Chen, C.L.P.: Multifocus image fusion with enhanced linear spectral clustering and fast depth map estimation. Neurocomputing 318, 43–54 (2018)

    Article  Google Scholar 

  16. Paul, S., Sevcenco, I.S., Agathoklis, P.: Multi-exposure and multi-focus image fusion in gradient domain. J. Circuits Syst. Comput. 25(10), 1650123 (2016)

    Article  Google Scholar 

  17. Li, S., Kang, X., Hu, J., Yang, B.: Image matting for fusion of multi-focus images in dynamic scenes. Inf. Fusion 14(2), 147–162 (2013)

    Article  Google Scholar 

  18. Bai, X., Zhang, Y., Zhou, F., Xue, B.: Quadtree-based multi-focus image fusion using a weighted focus-measure. Inf. Fusion 22, 105–118 (2015)

    Article  Google Scholar 

  19. Li, H.; Wu, X.-J.: Multi-focus image fusion using dictionary learning and low-rank representation. In:International Conference on Image and Graphics, pp. 675–686 (2017)

  20. Li, Q., Yang, X., Wu, W., Liu, K., Jeon, G.: Multi-focus image fusion method for vision sensor systems via dictionary learning with guided filter. Sensors 18(7), 2143 (2018)

    Article  Google Scholar 

  21. Amin, B., Riaz, M.M., Ghafoor, A.: A hybrid defocused region segmentation approach using image matting. Multidimensional Syst. Signal Process. 30, 561–569 (2019)

    Article  Google Scholar 

  22. Vu, C.T., Phan, T.D., Chandler, D.M.: S3: A spectral and spatial measure of local perceived sharpness in natural images. IET Image Process. 21(3), 934–945 (2012)

    Article  Google Scholar 

  23. Yi, X., Eramian, M.: LBP-based segmentation of defocus blur. IEEE Trans. Image Process. 25(4), 1626–1638 (2016)

    Article  MathSciNet  Google Scholar 

  24. Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 228–242 (2008)

    Article  Google Scholar 

  25. http://home.ustc.edu.cn/ liuyu1/.

  26. Xydeas, C.S., Petrovi, V.: Objective image fusion performance measure. Electron. Lett. 36(4), 308–309 (2000)

    Article  Google Scholar 

  27. Chen, Y., Blum, R.S.: A new automated quality assessment algorithm for image fusion. Image Vision Comput. 27(10), 1421–1432 (2009)

    Article  Google Scholar 

  28. Haghighat, M.; Razian, M. A.: Fast-fmi: non-reference image fusion metric. In: IEEE International Conference on Application of Information and Communication Technologies, pp. 1–3 (2014)

  29. Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002)

    Article  Google Scholar 

  30. Yang, C., Zhang, J., Wang, X., Liu, X.: A novel similarity based quality metric for image fusion. Inf. Fusion 9(2), 156–160 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Space Technology (IST), Islamabad, Pakistan

    Benish Amin

  2. National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Abdul Ghafoor

  3. Center for Advanced Studies in Telecommunication (CAST), COMSATS, Islamabad, Pakistan

    M. Mohsin Riaz

Authors
  1. Benish Amin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdul Ghafoor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Mohsin Riaz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdul Ghafoor.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amin, B., Ghafoor, A. & Riaz, M.M. Multi-focus Image Fusion Using Hybrid De-focused Region Segmentation Approach. Arab J Sci Eng 47, 1537–1545 (2022). https://doi.org/10.1007/s13369-021-05795-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-021-05795-1

Keywords

Search

Navigation