A Novel Multi-exposure Image Fusion Approach Based on Parameter Dynamic Selection

  • Yuanyuan Li
  • Mingyao ZhengEmail author
  • Hexu Hu
  • Huan Wang
  • Zhiqin Zhu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 528)


This paper propose a parameter dynamic selection approach for multi-exposure image fusion (MEF) that based on image cartoon-texture and structural patch decomposition. The image texture component is obtained by using texture-cartoon decomposition from the input image. The dynamic parameter is achieved by calculating the image texture entropy. The image patch is divided into three conceptually independent components by using structural patch decomposition. Respectively processing and fusing these three components, a fusion patch and aggregate fused patches are reconstruct into a fused image. This novel MEF method achieves dynamic parameter selection by utilizing texture-cartoon decomposition to obtain fusion images with more details.


Multi-exposure image fusion High dynamic range imaging Parameter dynamic selection Structural patch decomposition 


  1. 1.
    T. Mertens, J. Kautz, F. Van Reeth, Exposure fusion: a simple and practical alternative to high dynamic range photography. Comput. Gr. Forum 161–171 (2009)Google Scholar
  2. 2.
    J. Wang, D. Xu, B. Li, Exposure fusion based on steerable pyramid for displaying high dynamic range scenes. Spie Opt. Eng. 48(11) 117003–1170010 (2009)Google Scholar
  3. 3.
    J. Wang, H. Liu, N. He, Exposure fusion based on sparse representation using approximate K-SVD. Neurocomputing 135(135), 145–154 (2014)CrossRefGoogle Scholar
  4. 4.
    J. Shen, Y. Zhao, S. Yan, X. Li, Exposure fusion using boosting Laplacian pyramid. IEEE Transactions on Cybernetics 44(9), 1579–1590 (2014)CrossRefGoogle Scholar
  5. 5.
    R. Shen, I. Cheng, J. Shi, A. Basu, Generalized random walks for fusion of multi-exposure images. IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc. 20(12), 3634–3646 (2011)MathSciNetCrossRefGoogle Scholar
  6. 6.
    R. Shen, I. Cheng, A. Basu, QoE-based multi-exposure fusion in hierarchical multivariate Gaussian CRF. IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc. 22(6), 2469–2478 (2012)MathSciNetCrossRefGoogle Scholar
  7. 7.
    K. Hara, K. Inoue, K. Urahama, A differentiable approximation approach to contrast-aware image fusion. IEEE Signal Process. Lett. 21(6), 742–745 (2014)CrossRefGoogle Scholar
  8. 8.
    T.H. Oh, J.Y. Lee, T.Y. Wing, I.S. Kweon, Robust high dynamic range imaging by rank minimization. IEEE Trans. Pattern Anal. Mach. Intell. 73(6), 1219–1232 (2015)CrossRefGoogle Scholar
  9. 9.
    M. Song, D. Tao, C. Chen, J. Bu, J. Luo, C. Zhang, Probabilistic exposure fusion. IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc. 21(1), 341–357 (2012)MathSciNetCrossRefGoogle Scholar
  10. 10.
    M. Bertalmio, S. Levine, Variational Approach for the Fusion of Exposure Bracketed Pairs. IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc. 22(2), 721–723 (2013)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  12. 12.
    K. Ma, H. Li, H. Yong, Z. Wang, D. Meng, L. Zhang, Robust Multi-exposure image fusion: a structural patch decomposition approach. IEEE Trans. Image Process. 26(5), 2519–2532 (2017)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Z.Q. Zhu, H. Yin, Y. Chai, Y. Li, G. Qi, A novel multi-modality image fusion method based on image decomposition and sparse representation. Inf. Sci. 432, 516–529 (2017)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Z. Zhu, Y. Chai, H. Yin, Y. Li, Z. Liu, A novel dictionary learning approach for multi-modality medical image fusion. Neurocomputing 214, 471–482 (2016)Google Scholar
  15. 15.
    K. Wang, G. Qi, Z. Zhu, Y. Chai, A novel geometric dictionary construction approach for sparse representation based image fusion. Entropy 19(7), 306 (2017)CrossRefGoogle Scholar
  16. 16.
    K. Ma, Z. Duanmu, H. Yeganeh, Z. Wang, Multi-exposure image fusion by optimizing a structural similarity index. IEEE Trans. Comput. Imaging 4(1), 60–72 (2017)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yuanyuan Li
    • 1
  • Mingyao Zheng
    • 1
    Email author
  • Hexu Hu
    • 1
  • Huan Wang
    • 1
  • Zhiqin Zhu
    • 1
  1. 1.School of AutomationChongqing University of Posts and TelecommunicationsChongqingChina

Personalised recommendations