Skip to main content
Log in

Research on multi-focus image fusion algorithm based on total variation and quad-tree decomposition

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In order to get the focus region from the two or more source image more effectively and to represent the source image completely and effectively, a decomposition strategy based on the combination of total variation and quad-tree and an improved fusion method of focus region detection are proposed. The theory of total variation and quad-tree is applied to the fusion of multi-focus images. Firstly, two registered experimental images are decomposed into the optimal block decomposition graph by total variation and quad-tree decomposition, respectively. For each block, the initial focus region decision map of the source image is found by using improved Sum-Modified-Laplacian, and the final focus region decision map is obtained by consistency test and morphological processing for the initial focus region decision map. Furthermore, compared with other algorithms, the improved algorithm has more advantages in extracting the focus area, because of improved focus evaluation function and more accurate detection of the focus area. According to the fusion source image of the final focus region decision map, the results of four sets of experiments show that the fusion quality and effect are significantly improved compared with the existing algorithm.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Bhateja V, Patel H, Krishn A et al (2015) Multimodal medical image sensor fusion framework using cascade of wavelet and contourlet transformdomains. IEEE Sensors J 15(12):6783–6790

    Article  Google Scholar 

  2. Buades A, Le TM, Morel J et al (2010) Fast cartoon + texture image filters[J]. IEEE Trans Image Process 19(8):1978–1986

  3. Chen Z, Yang X, Zhang C et al (2016) Infrared and visible image fusion based on the compensation mechanism in NSCT domain[J]. Chinese Journal of Scientific Instrument

  4. Cheng J, Liu H, Liu T et al (2015) Remote sensing image fusion via wavelet transform and sparse representation[J]. Isprs Journal of Photogrammetry & Remote Sensing 104:158–173

    Article  Google Scholar 

  5. De I, Chanda B (2013) Multi-focus image fusion using a morphology-based focus measure in a quad-tree structure. Inf. Fusion 14(2):136–146

    Article  Google Scholar 

  6. Ellmauthaler A, Pagliari CL, Silva EABD (2013) Multiscale image fusion using the undecimated wavelet transform with spectral factorization andnonorthogonal filter banks. IEEE Trans Image Process 22(03):1005–1017

    Article  MathSciNet  Google Scholar 

  7. Fakhari F, Mosavi MR, Lajvardi MM (2017) Image Fusion based on Multi-scale Transform and Sparse Representation: Image Energy Approach [J]. Iet Image Processing, 11(11)

  8. Fu Z, Wang X, Xu J et al (2016) Infrared and visible images fusion based on RPCA and NSCT [J]. Infrared Physics & Technology, S1350449516300330

  9. Gao R, Vorobyov S, Zhao H (2017) Image fusion with Cosparse analysis operator[J]. IEEE Signal Processing Letters:1–1

  10. Jameel A, Ghafoor A, Riaz MM (2015) Wavelet and guided filter based multifocus fusion for noisy images. Optik 126(23):3920–3923

    Article  Google Scholar 

  11. Koley S, Galande A, Kelkar B, Sadhu AK, Sarkar D, Chakraborty C (2016) Multispectral MRI image fusion for enhanced visualization of meningioma brain tumors and edema using contourlet transform and fuzzy statistics. Journal of Medical and Biological Engineering 36(4):470–484

  12. Li ST, Kang XD, Hu JW, Yang B (2013) Image matting for fusion of multi-focus images in dynamic scenes. Inf Fusion 14(2):147–162

    Article  Google Scholar 

  13. Li H, Qiu H, Yu Z, Zhang Y (2016) Infrared and visible image fusion scheme based on NSCT and low-level visual features. Infrared Phys Technol 76:174–184

  14. Liu B, Liu WJ, Luo YH et al (2016) Construction of eight channel multi-resolution singular value decomposition of matrix and its application in multi-focus image fusion[J]. Acta Electron Sin 24(9):3297–3327

    Google Scholar 

  15. Liu Y, Chen X, Peng H et al (2017) Multi-focus image fusion with a deep convolutional neural network[J]. Information Fusion 36:191–207

    Article  Google Scholar 

  16. Meyer Y (2001) Oscillating patterns in image processing and nonlinear evolution equations: the fifteenth dean Jacqueline B. Lewis memorial lectures [J]. Of University Lecture 22:122

  17. Patil U, Mudengudi U (2011) Image fusion using hierarchical PCA[C]// International Conference on Image Information Processing. IEEE, 1–6

  18. Srivastava R, Khare A, Prakash O (2016) Local energy-based multimodal medical image fusion in curvelet domain. IET Comput Vis 10(6):513–527

  19. Tilly N, Aasen H, Bareth G (2015) Fusion of plant height and vegetation indices for the estimation of barley biomass. Remote Sens 7(9):11449–11480

  20. Wan T, Zhu CC, Qin ZC (2013) Multi-focus image fusion based on robust principal component analysis[J]. Pattern Recogn Lett 34(9):1001–1008

    Article  Google Scholar 

  21. Wang Q, Nie RC, Jin X et al (2016) Image Fusion Algorithm Using LP Transformation and PCNN-SML[J]. Computer Science

  22. Xu J, Tai XC, Wang LL (2017) A two-level domain decomposition method for image restoration [J]. Inverse Problems and Imaging (IPI) 4(3):523–545

    Article  MathSciNet  Google Scholar 

  23. Yang Y, Tong S, Huang SY (2015) Multifocus image fusion based on NSCT and focused area detection. IEEE Sensors J 15(05):2824–2838

    Google Scholar 

  24. Yin M, Pang J, Wei Y et al (2016) Image fusion algorithm based on nonsubsampled dual-tree complex contourlet transform and compressive sensing pulse coupled neural network[J]. Journal of Computer-Aided Design & Computer Graphics

  25. Zhang Y, Bai X, Wang T (2017) Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure[J]. Information Fusion 35:81–101

    Article  Google Scholar 

  26. Zhang Y, Zhang L, Bai X et al (2017) Infrared and visual image fusion through infrared feature extraction and visual information preservation[J]. Infrared Physics & Technology, 83

  27. Zhu QB, Ding SF (2016) Self-adaptation NSCT-PCNN image fusion based GA optimization[J]. Journal of Chinese Computer Systems 37(7):1583–1587

    Google Scholar 

Download references

Acknowledgments

The authors wish to express their sincere thanks to the editors and that of the referees concerning improvement of this paper. The authors were supported financially by National Natural Science Foundations of Hunan Province, China (No. 2018JJ3079), National Science and Technology Support Program of the Ministry of Science and Technology of China (No.2015BAF13B00).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jianxu Mao.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, C., Wang, X. & Mao, J. Research on multi-focus image fusion algorithm based on total variation and quad-tree decomposition. Multimed Tools Appl 79, 10475–10488 (2020). https://doi.org/10.1007/s11042-019-7563-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7563-y

Keywords

Navigation