Skip to main content
Log in

Review of pixel-level image fusion

  • Published:
Journal of Shanghai Jiaotong University (Science) Aims and scope Submit manuscript

Abstract

Image fusion can be performed at different levels: signal, pixel, feature and symbol levels. Almost all image fusion algorithms developed to date fall into pixel level. This paper provides an overview of the most widely used pixel-level image fusion algorithms and some comments about their relative strengths and weaknesses. Particular emphasis is placed on multiscale-based methods. Some performance measures practicable for pixel-level image fusion are also discussed. At last, prospects of pixel-level image fusion are made.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Abidi M A, Gonzalez R C. Data fusion in robotics and machine intelligence [M]. San Diego, CA: Academic Press, 1992.

    Google Scholar 

  2. Smith M I, Heather J P. Review of image fusion technology in 2005 [J]. Proc SPIE, 2005, 5783: 29–45.

    Article  Google Scholar 

  3. Ferris D D, Mcmillan R W, Currie N C, et al. Sensors for military special operations and law enforcement applications [J]. Proc SPIE, 1997, 3062: 173–180.

    Article  Google Scholar 

  4. Smith M I, Ball A, Hooper D. Real-time image fusion: A vision aid for helicopter pilotage [J]. Proc SPIE, 2002, 4713: 83–94.

    Article  Google Scholar 

  5. Hill D, Edwards P, Hawkes D. Fusing medical images [J]. Image Processing, 1994, 6(2): 22–24.

    Google Scholar 

  6. Qu G H, Zhang D L, Yan P E. Medical image fusion by wavelet transform modulus maxima [J]. Opt Express, 2001, 9(4): 184–190.

    Article  Google Scholar 

  7. Daniel M M, Willsky A S. A multiresolution methodology for signal-level fusion and data assimilation with applications to remote sensing [J]. Proc IEEE, 1997, 85(1): 164–180.

    Article  Google Scholar 

  8. Slamani M A, Ramac L, Uner M, et al. Enhancement and fusion of data for concealed weapons detection [J]. Proc SPIE, 1997, 3068: 8–19.

    Article  Google Scholar 

  9. Zhang Z, Blum R S. A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application [J]. Proc IEEE, 1999, 87(8): 1315–1326.

    Article  Google Scholar 

  10. Toet A, Ijspeert J K, Waxman A M, et al. Fusion of visible and thermal imagery improves situational awareness [J]. Proc SPIE, 1997, 3088: 177–188.

    Article  Google Scholar 

  11. Toet A, Franken E M. Perceptual evaluation of different image fusion schemes [J] Displays, 2003, 24: 25–37.

    Article  Google Scholar 

  12. Rockinger O, Fechner T. Pixel-level image fusion: The case of image sequences [J]. Proc SPIE, 1998, 3374: 378–388.

    Article  Google Scholar 

  13. Brown L G. A survey of image registration techniques [J]. ACM Computing Surveys, 1992, 24(4): 325–376.

    Article  Google Scholar 

  14. Jia Y H. Fusion of landsat TM and SAR images based on principal component analysis [J]. Remote Sensing Technology and Application, 1998, 13(1): 46–49.

    Google Scholar 

  15. Marr D. Vision [M]. San Francisco, CA: W H Freeman Press, 1982.

    Google Scholar 

  16. Burt P J, Adelson E. The Laplacian pyramid as a compact image code [J]. IEEE Trans Commun, 1983, 31(4): 532–540.

    Article  Google Scholar 

  17. Burt P J, Adelson E H. Merging images through pattern decomposition [J]. Proc SPIE, 1985, 575: 173–181.

    Google Scholar 

  18. Burt P J, Kolczynski R J. Enhanced image capture through fusion [C] // Proc 4th Int Conf Computer Vision. Berlin, Germany: IEEE Press, 1993: 173–182.

    Google Scholar 

  19. Toet A. Hierarchical image fusion [J]. Mach Vision Appl, 1990, 3: 1–11.

    Article  Google Scholar 

  20. Mallat S G. A theory for multiresolution signal decomposition: The wavelet representation [J]. IEEE Trans Pattern Anal Machine Intell, 1989, 11(7): 674–693.

    Article  MATH  Google Scholar 

  21. Vetterli M, Herley C. Wavelets and filter banks: Theory and design [J]. IEEE Trans Signal Process, 1992, 40(9): 2207–2232.

    Article  MATH  Google Scholar 

  22. Li H, Manjunath B S, Mitra S K. Multisensor image fusion using the wavelet transform [J]. Graph Models Image Process, 1995, 57(3): 235–245.

    Article  Google Scholar 

  23. Chipman L J, Orr T M, Lewis L N. Wavelets and image fusion [C]// Proc IEEE ICIP3. Washington D C: IEEE Press, 1995: 248–251.

    Google Scholar 

  24. Unser M. Texture classification and segmentation using wavelet frames [J]. IEEE Trans Image Process, 1995, 4(11): 1549–1560.

    Article  Google Scholar 

  25. Hill P, Canagarajah N, Bull D. Image fusion using complex wavelets [C]// Proc BMVC. Cardiff, UK: British Machine Vision Association Press, 2002: 487–496.

    Google Scholar 

  26. Kingsbury N G. Complex wavelets for shift invariant analysis and filtering of signals [J]. Applied and Computational Harmonic Analysis, 2001, 10(3): 234–253.

    Article  MATH  MathSciNet  Google Scholar 

  27. Yang B, Jing Z L. Image fusion using a low-redundant and nearly shift-invariant discrete wavelet frame [J]. Optical Engineering, 2007, 46(10): 107002.

    Article  Google Scholar 

  28. Huntsberger T, Jawerth B. Wavelet based sensor fusion [J]. Proc SPIE, 1993, 2059: 488–498.

    Article  Google Scholar 

  29. Piella G. A region-based multiresolution image fusion algorithm [C]// ISIF Fusion 2002 Conference. Annapolis: ISIF, 2002: 1557–1564.

    Google Scholar 

  30. Zhang Z, Blum R. Region-based image fusion scheme for concealed weapon detection [C]//Proc 31st Annual Conference on Information Sciences and Systems. Baltimore, USA: John Hopkins University Press, 1997: 168–173.

    Google Scholar 

  31. Lewis J J, O’callaghan R J, Nikolov S G, et al. Region based fusion using complex wavelets [C]//7th International Conference on Information Fusion. Stockholm, Sweden: ISIF, 2004: 555–562.

    Google Scholar 

  32. Xiao G, Jing Z L, Wu J M, et al. Synthetically evaluation system for multi-source image fusion and experimental analysis [J]. Journal of Shanghai Jiaotong University (Science), 2006, E-11(3): 263–270.

    Google Scholar 

  33. Xydeas C S, Petrović V. Objective image fusion performance measure [J]. Electronics Lett, 2000, 36(4): 308–309.

    Article  Google Scholar 

  34. Mckeown D M, Cochran S D, Ford S J, et al. Fusion of HYDlCE hyper spectral data with panchromatic imagery for cartographic feature extraction [J]. IEEE Trans Geosciences and Remote Sensing, 1999, 37(3): 1261–1277.

    Article  Google Scholar 

  35. Laliberte F, Gagnon I, Sheng Y I. Registration and fusion of retinal images:An evaluation study [J]. IEEE Trans Medical Imaging, 2003, 22(5): 661–673.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bo Yang  (杨 波).

Additional information

Foundation item: the National Natural Science Foundation of China (Nos. 60775022 and 60705006)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yang, B., Jing, Zl. & Zhao, Ht. Review of pixel-level image fusion. J. Shanghai Jiaotong Univ. (Sci.) 15, 6–12 (2010). https://doi.org/10.1007/s12204-010-7186-y

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12204-010-7186-y

Key words

CLC number

Navigation