Skip to main content
SpringerLink
Log in

Renyi entropy and atom search sine cosine algorithm for multi focus image fusion

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

This paper proposes a novel strategy, atom search sine cosine algorithm (ASSCA) for multi-focus image fusion. Here, the discrete wavelet transform (DWT) is adapted for transforming images into sub-bands. The fusion is carried out using a fusion rule based on weighting criteria that uses two attributes, Renyi entropy and the proposed ASSCA. Entropy discovers the entropy fusion factor considering the assessed entropy from the source image. Further, an optimization strategy, ASSCA is developed by integrating atom search optimization and sine cosine algorithm for precise selection of fusion factor. The output obtained from the fusion undergoes inverse discrete wavelet transform to obtain the resultant fused image. The proposed DWT + ASSCA + Renyi entropy outperformed other methods with maximal mutual information of 1.492, maximal peak signal-to-noise ratio of 40.625 dB, minimal root mean-squared error of 7.651, maximum correlation coefficient of 0.988, and minimum deviation index of 1.146.

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

Similar content being viewed by others

Abbreviations

JHE:

Joint holo-entropy

HWFusion:

Holoentropy whale fusion

UDWT:

Undecimated discrete wavelet transform

GA:

Genetic algorithm

SP-Whale:

Whale optimization algorithm + particle swarm optimization

coif1:

Coiflets 1 wavelet

db2:

Daubechies 2 wavelet

sym2:

Symlets 2 wavelet transform

PCNN:

Pulse coupled neural network

DCNN:

Deep convolutional neural network

CA:

Approximation coefficients array

CD:

Diagonal detailed coefficients array

CH:

Horizontal detail coefficients array

CV:

Vertical detail coefficients array

i.e.:

That is

References

  1. Tian, Jing, Chen, Li, Ma, Lihong, Yu, W.: Multi-focus image fusion using a bilateral gradient-based sharpness criterion. Opt. Commun. 284, 80–87 (2011)

    Article  Google Scholar 

  2. Wang, Zhaobin, Ma, Y., Gu, J.: Multi-focus image fusion using PCNN. Pattern Recognit. 43(6), 2003–2016 (2010)

    Article  Google Scholar 

  3. Li, Shutao, Kang, Xudong, Jianwen, Hu, Yang, Bin: Image matting for fusion of multi-focus images in dynamic scenes. Inf. Fusion 14, 147–162 (2013)

    Article  Google Scholar 

  4. Venkatrao, P.H., Damodar, S.S.: HWFusion: holoentropy and SP-Whale optimisation-based fusion model for magnetic resonance imaging multimodal image fusion. IET Image Process. 12(4), 572–581 (2018)

    Article  Google Scholar 

  5. Farid, M.S., Mahmooda, S., Al-Maadeeda, S.A.: Multi-focus image fusion using content adaptive blurring. Inf. Fusion 45, 96–112 (2019)

    Article  Google Scholar 

  6. Pajares, Gonzalo, de la Cruz, J.: A wavelet-based image fusion tutorial’. Pattern Recognit. 37(9), 1855–1872 (2004)

    Article  Google Scholar 

  7. Haghighat, Mohammad Bagher Akbari, Aghagolzadeh, Ali, Seyedarabi, Hadi: Multi-focus image fusion for visual sensor networks in DCT domain. Comput. Electr. Eng. 37, 789–797 (2011)

    Article  Google Scholar 

  8. Li, Huafeng, Chai, Yi, Li, Zhaofei: Multi-focus image fusion based on nonsubsampled contourlet transform and focused regions detection. Optik 124, 40–51 (2013)

    Article  Google Scholar 

  9. Liu, Shuaiqi, Wang, Jie, Yucong, Lu, Li, Hailiang, Zhao, Jie, Zhu, Zhihui: Multi-focus image fusion based on adaptive dual-channel spiking cortical model in non-subsampled shearlet domain. IEEE Access 7, 56367–56388 (2019)

    Article  Google Scholar 

  10. Huang, W., Jing, Z.: Evaluation of focus measures in multi-focus image fusion. Pattern Recognit. Lett. 28, 493–500 (2018)

    Article  Google Scholar 

  11. Yang, Yong, Ding, Min, Huang, Shuying, Que, Yue, Wan, W., Yang, M., Sun, J.: Multi-focus image fusion via clustering PCA based joint dictionary learning. IEEE Access 5, 16985–16997 (2017)

    Article  Google Scholar 

  12. Li, S.T., Kang, X.D., Hu, J.W.: Image fusion with guided filtering. IEEE Trans. Image Process. 22, 2864–2875 (2013)

    Article  Google Scholar 

  13. Wang, K., Qi, G., Zhu, Z., Chai, Y.: A novel geometric dictionary construction approach for sparse representation based image fusion. Entropy 19, 306–323 (2017)

    Article  Google Scholar 

  14. Li, X., Wang, L., Wang, J., Zhang, X.: Multi-focus image fusion algorithm based on multilevel morphological component analysis and support vector machine. IET Image Process. 11(10), 919–926 (2017)

    Article  Google Scholar 

  15. Guruprasad, S., Kurian, M.Z., Suma, H.N., Sharanabasavaraj, : A medical multi-modality image fusion of CT/PET with PCA, DWT methods. J. Dent. Mater. Tech. 4(2), 677–681 (2013)

    Google Scholar 

  16. El-Gamal, F.E.-Z.A., Elmogy, M., Atwan, A.: Current trends in medical image registration and fusion. Egypt. Inform. J. 17(1), 99–124 (2016)

    Article  Google Scholar 

  17. Zhao, Wenda, Wang, Dong, Lu, Huchuan: Multi-focus image fusion with a natural enhancement via joint multi-level deeply supervised convolutional neural network. IEEE Trans. Circuits Syst. Video Technol. 29(4), 1102–1115 (2019)

    Article  Google Scholar 

  18. Yang, Y., Tong, S., Huang, S., Lin, P., Fang, Y.: A hybrid method for multi-focus image fusion based on fast discrete curvelet transform. IEEE Access 5, 14898–14913 (2017)

    Article  Google Scholar 

  19. Huang, Ying, Li, Weisheng, Gao, Mingliang, Liu, Zheng: Algebraic multi-grid based multi-focus image fusion using watershed algorithm. IEEE Access 6, 47082–47091 (2018)

    Article  Google Scholar 

  20. Jiang, Q., Jin, X., Lee, S.-J., Yao, S.: A novel multi-focus image fusion method based on stationary wavelet transform and local features of fuzzy sets. IEEE Access 5, 20286–20302 (2017)

    Article  Google Scholar 

  21. Lai, Rui, Li, Yongxue, Guan, Juntao, Xiong, Ai: Multi-scale visual attention deep convolutional neural network for multi-focus image fusion. IEEE Access 7, 114385–114399 (2019)

    Article  Google Scholar 

  22. Zheng, Y., Qin, Z., Shao, L.: X Hou,” A novel objective image quyality metric for image fusion based on Renyi entropy”. Inf. Technol. J. 7(6), 930–935 (2008)

    Article  Google Scholar 

  23. Zhao, Weiguo, Wang, Liying, Zhang, Zhenxing: Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowl. Based Syst. 163, 283–304 (2019)

    Article  Google Scholar 

  24. Mirjalili, Seyedali: SCA: a sine cosine algorithm for solving optimization problems. Knowl. Based Syst. 96, 120–133 (2016)

    Article  Google Scholar 

  25. Lytro Multi-focus Image Dataset taken from, https://www.researchgate.net/publication/291522937_Lytro_Multi-focus_Image_Dataset. Accessed Oct 2019

  26. Kavitha, S., Thyagharajan, K.K.: Efficient DWT-based fusion techniques using genetic algorithm for optimal parameter estimation. Soft. Comput. 21(12), 3307–3316 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CSE Department, Harcourt Butler Technical University, Kanpur, India

    Vineeta Singh & Vandana Dixit Kaushik

Authors
  1. Vineeta Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vandana Dixit Kaushik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vineeta Singh.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 612 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, V., Kaushik, V.D. Renyi entropy and atom search sine cosine algorithm for multi focus image fusion. SIViP 15, 903–912 (2021). https://doi.org/10.1007/s11760-020-01814-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-020-01814-0

Keywords

Search

Navigation