Robust Image Fusion Using Stationary Wavelet Transform

정상 웨이블렛 변환을 이용한 로버스트 영상 융합

  • Received : 20110800
  • Accepted : 20111000
  • Published : 2011.12.31


Image fusion is the process of combining information from two or more source images of a scene into a single composite image with application to many fields, such as remote sensing, computer vision, robotics, medical imaging and defense. The most common wavelet-based fusion is discrete wavelet transform fusion in which the high frequency sub-bands and low frequency sub-bands are combined on activity measures of local windows such standard deviation and mean, respectively. However, discrete wavelet transform is not translation-invariant and it often yields block artifacts in a fused image. In this paper, we propose a robust image fusion based on the stationary wavelet transform to overcome the drawback of discrete wavelet transform. We use the activity measure of interquartile range as the robust estimator of variance in high frequency sub-bands and combine the low frequency sub-band based on the interquartile range information present in the high frequency sub-bands. We evaluate our proposed method quantitatively and qualitatively for image fusion, and compare it to some existing fusion methods. Experimental results indicate that the proposed method is more effective and can provide satisfactory fusion results.


  1. Arivazhagan, S., Ganesan, L. and Subash Kumar, T. G. (2009). A modified statistical approach for image fusion using wavelet transform, Signal, Image and Video Processing, 3, 137-144.
  2. Aslantas, V. and Kurban, R. (2009). A comparison of criterion functions for fusion of multi-focus noisy images, Optics Communications, 282, 3231-3242.
  3. Fowler, J. E. (2005). The redundant discrete wavelet transform and additive noise, IEEE Signal Processing Letters, 12, 629-632.
  4. Fridman, P. A. (2008). Statistically stable estimates of variance in radio-astronomy observations as tools for radio-frequency interference mitigation, The Astronomical Journal, 135, 1810-1824.
  5. Ganzalo, P. and Jesus, M. (2004). Wavelet-based image fusion tutorial, Pattern Recognition, 8, 1855-1872.
  6. Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J. and Stahel, W. A. (1986). Robust Statistics: The Approach Based on In uence Functions, Wiley, New York.
  7. Li, H., Manunath, B. S. and Mitra, S. K. (1995). Multisensor image fusion using the wavelet transform, Graphical Models and Image Processing, 57, 235-245.
  8. Li, X., He, M. and Roux, M. (2008). Multifocus image fusion based on redundant wavelet transform, IET Image Processing, 4, 283-293.
  9. Ma, H., Jia, C. and Liu, S. (2005). Multisource image fusion based on wavelet transform, International Journal of Information Technology, 11, 81-91.
  10. Park, M. J., Kwon, M. J., Kim, G. H., Shim, H. S. and Lim, D. H. (2011). Image fusion based on statistical hypothesis test using wavelet transform, The Korean Journal of Applied Statistics, 24, 695-708.
  11. Rousseeuw, P. J. and Croux, C. (1993). Alternatives to the median absolute deviation, Journal of the American Statistical Association, 88, 1273-1283.
  12. Samworth, R. J. and Wand, M. P. (2010). Asymptotics and optimal bandwidth selection for highest density region estimation, Annals of Statistics, 38, 1767-1792.
  13. Sasikala, M. and Kumaravel, N. (2007). A comparative analysis of feature based image fusion methods, Information Technology Journal, 6, 1224-1230.
  14. Shensa, M. J. (1992). The discrete wavelet transform: Wedding the a trous and mallat algorithm, IEEE Transaction on Signal Processing, 40, 2464-2482.
  15. Starck, J. L. and Murtagh, F. (1994). Image restoration with noise suppression using the wavelet transform, Astronomy and Astrophysics, 288, 342-348.
  16. Yang, Y. (2011). Multiresolution image fusion based on wavelet transform by using a novel technique for selection coefficients, Journal of Multimedia, 6, 91-98.
  17. Zhang, Z. and Blum, R. S. (1999). A categorization of multiscale decomposition-based image fusion schemes with a performance study for a digital camera application, Proceedings of the IEEE, 87, 1315-1326.
  18. Zhao, R. Z., Xu, L. and Song, G. X. (2002). Multiscale image data fusion with wavelet transform, Journal of Computer-Aided Design & Computer Graphics, 14, 361-364.