OSA's Digital Library

Applied Optics

Applied Optics


  • Vol. 51, Iss. 12 — Apr. 20, 2012
  • pp: 1910–1921

Fusion of infrared and visible images based on focus measure operators in the curvelet domain

Shao Zhenfeng, Liu Jun, and Cheng Qimin  »View Author Affiliations

Applied Optics, Vol. 51, Issue 12, pp. 1910-1921 (2012)

View Full Text Article

Enhanced HTML    Acrobat PDF (1346 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Aiming at the differences of physical characteristics between infrared sensors and visible ones, we introduce the focus measure operators into the curvelet domain in order to propose a novel image fusion method. First, the fast discrete curvelet transform is performed on the original images to obtain the coefficient subbands in different scales and various directions, and the focus measure values are calculated in each coefficient subband. Then, the local variance weighted strategy is employed to the low-frequency coefficient subbands for the purpose of maintaining the low-frequency information of the infrared image and adding the low-frequency features of the visible image to the fused image; meanwhile, the fourth-order correlation coefficient match strategy is performed to the high-frequency coefficient subbands to select the suitable high-frequency information. Finally, the fused image can be obtained through the inverse curvelet transform. The practical experiments indicate that the presented method can integrate more useful information from the original images, and the fusion performance is proved to be much better than the traditional methods based on the wavelet, curvelet, and pyramids.

© 2012 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(100.2980) Image processing : Image enhancement

ToC Category:
Image Processing

Original Manuscript: November 29, 2011
Revised Manuscript: February 7, 2012
Manuscript Accepted: February 7, 2012
Published: April 11, 2012

Shao Zhenfeng, Liu Jun, and Cheng Qimin, "Fusion of infrared and visible images based on focus measure operators in the curvelet domain," Appl. Opt. 51, 1910-1921 (2012)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. F. F. Zhou, W. D. Chen, and L. F. Li, “Fusion of IR and visible images using region growing,” J. Appl. Opt. 28, 737–741 (2007) (in Chinese).
  2. A. Apatean, C. Rusu, A. Rogozan, and A. Bensrhair, “Visible-infrared fusion in the frame of an obstacle recognition system,” in IEEE International Conference on Automation, Quality and Testing, Robotics (IEEE, 2010), pp. 1–6.
  3. X. H. Yang, H. Y. Jin, and L. C. Jiao, “Adaptive image fusion algorithm for infrared and visible light images based on DT-CWT,” J. Infrared Millim. Waves 26, 419–424 (2007).
  4. H. M. Wang, K. Zhang, and Y. J. Li, “Image fusion algorithm based on wavelet transform,” Infrared Laser Eng. 34, 328–332 (2005) (in Chinese).
  5. S. Firooz, “Comparative image fusion analysis,” in IEEE Proceedings of the Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 1–8.
  6. G. Pajares and J. M. de la Cruz, “A wavelet-based image fusion tutorial,” Pattern Recogn. 37, 1855–1872 (2004). [CrossRef]
  7. R. Minhas, A. A. Mohammed, and Q. M. J. Wu, “Shape from focus using fast discrete curvelet transform,” Pattern Recogn. 44, 839–853 (2011). [CrossRef]
  8. S. Li and B. Yang, “Multifocus image fusion by combining curvelet and wavelet transform,” Pattern Recogn. Lett. 29, 1295–1301 (2008). [CrossRef]
  9. X. B. Qu, J. W. Yan, and G. D. Yang, “Multifocus image fusion method of sharp frequency localized contourlet transform domain based on sum-modified-Laplacian,” Opt. Precision Eng. 17, 1203–1212 (2009).
  10. E. J. Candès and D. L. Donoho, “New tight frames of curvelets and optimal representations of objects with C2 singularities,” Commun. Pure Appl. Math. 57, 219–266 (2004). [CrossRef]
  11. E. J. Candès, L. Demanet, and D. L. Donoho, “Fast discrete curvelet transforms,” Multiscale Model. Simul. 5, 861–899 (2006). [CrossRef]
  12. W. Huang, and Z. L. Jing, “Evaluation of focus measures in multi-focus image fusion,” Pattern Recogn. Lett. 28, 493–500 (2007). [CrossRef]
  13. R. Minhas, A. A. Mohammed, and Q. M. J. Wu, “An efficient algorithm for focus measure computation in constant time,” IEEE Trans. Circuits Syst. Video Technol. 22: 152–156 (2012). [CrossRef]
  14. R. Minhas, A. A. Mohammed, Q. M. J. Wu, and M. A. Sid-Ahmed, “3D shape from focus and depth map computation using steerable filters,” Lect. Notes Comput. Sci. 5627, 573–583 (2009). [CrossRef]
  15. M. Muhammad and T. S. Choi, “Sampling for shape from focus in optical microscopy,” IEEE Trans. Pattern Anal. Machine Intell. 99, 1–12 (2011).
  16. V. Aslantas and R. Kurban, “A comparison of criterion functions for fusion of multi-focus noisy images,” Opt. Commun. 282, 3231–3242 (2009). [CrossRef]
  17. S. K. Nayar, and Y. Nakagawa, “Shape from focus,” IEEE Trans. Pattern Anal. Machine Intell. 16, 824–831 (1994). [CrossRef]
  18. Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process Lett. 9, 81–84 (2002). [CrossRef]
  19. G. X. Liu, S. G. Zhao, and W. H. Yang, “Multi-sensor image fusion scheme based on gradient pyramid decomposition,” J. Optoelectron. Laser 12, 293–296 (2001) (in Chinese).
  20. J. Liu and Z. F. Shao, “Feature-based remote sensing image fusion quality metrics using structure similarity,” Acta Photon. Sin. 40, 126–131 (2011). [CrossRef]
  21. M. Welling, “Robust higher order statistics,” in Proceedings of 10th International Workshop on Artificial Intelligence and Statistics (2005), pp. 405–412.

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited