A Similarity Metric for Assessment of Image Fusion Algorithms
In this paper, we present a novel objective nonreference
performance assessment algorithm for image fusion. It takes
into account local measurements to estimate how well the important
information in the source images is represented by the fused image.
The metric is based on the Universal Image Quality Index and uses
the similarity between blocks of pixels in the input images and the
fused image as the weighting factors for the metrics. Experimental
results confirm that the values of the proposed metrics correlate well
with the subjective quality of the fused images, giving a significant
improvement over standard measures based on mean squared error
and mutual information.
[1] H. Maitre and I. Bloch, "Image fusion", Vistas in Astronomy, Vol. 41,
No. 43, 1997, pp. 329-335.
[2] S. Nikolov and P. Hill and D. Bull and N. Canagarajah, Wavelets for
image fusion, in "Wavelets in Signal and Image Analysis", Kluwer,
Dordrecht, The Netherlands, 2001.
[3] D. Ryan and R. Tinkler, "Night pilotage assessment of image fusion",
Proc. SPIE, Orlando, FL, 1995, pp. 50-67.
[4] A. Toet and E. M. Franken "Perceptual evaluation of different image
fusion schemes", Displays, Vol. 24, No. 1, 2003, pp. 25-37.
[5] G. Piella, "A general framework for multiresolution image fusion: from
pixels to regions", Information Fusion, Vol. 4, No. 2003, 2003, pp. 259-
280.
[6] H. Li and B. S. Manjunath and S. K. Mitra, "Multisensor image fusion
using the wavelet transform", Graphical Models and Image Processing,
Vol. 57, No. 3, 1995, pp. 235-245.
[7] O. Rockinger, "Image sequence fusion using a shift invariant wavelet
transform", Proc. IEEE International Conference on Image Processing,
Washington, DC, 1997, pp.288-291.
[8] C. Xydeas and V. Petrovic, "Objective pixel-level image fusion performance
measure", Proc. SPIE, Orlando, FL, 2000, pp. 88-99.
[9] G. H. Qu and D. L. Zhang and P. F. Yan, "Information measure for
performance of image fusion", Electronics Letters, Vol. 38, No. 7, 2002,
pp. 313-315.
[10] Z. Wang and A. C. Bovik, "A universal image quality index", IEEE
Signal Processing Letters, Vol. 9, No. 3, 2002, pp. 81-84.
[11] G. Piella and H. Heijmans, "A new quality metric for image fusion",
Proc. IEEE International Conference on Image Processing, Barcelona,
Spain, 2003, pp. 173-176
[1] H. Maitre and I. Bloch, "Image fusion", Vistas in Astronomy, Vol. 41,
No. 43, 1997, pp. 329-335.
[2] S. Nikolov and P. Hill and D. Bull and N. Canagarajah, Wavelets for
image fusion, in "Wavelets in Signal and Image Analysis", Kluwer,
Dordrecht, The Netherlands, 2001.
[3] D. Ryan and R. Tinkler, "Night pilotage assessment of image fusion",
Proc. SPIE, Orlando, FL, 1995, pp. 50-67.
[4] A. Toet and E. M. Franken "Perceptual evaluation of different image
fusion schemes", Displays, Vol. 24, No. 1, 2003, pp. 25-37.
[5] G. Piella, "A general framework for multiresolution image fusion: from
pixels to regions", Information Fusion, Vol. 4, No. 2003, 2003, pp. 259-
280.
[6] H. Li and B. S. Manjunath and S. K. Mitra, "Multisensor image fusion
using the wavelet transform", Graphical Models and Image Processing,
Vol. 57, No. 3, 1995, pp. 235-245.
[7] O. Rockinger, "Image sequence fusion using a shift invariant wavelet
transform", Proc. IEEE International Conference on Image Processing,
Washington, DC, 1997, pp.288-291.
[8] C. Xydeas and V. Petrovic, "Objective pixel-level image fusion performance
measure", Proc. SPIE, Orlando, FL, 2000, pp. 88-99.
[9] G. H. Qu and D. L. Zhang and P. F. Yan, "Information measure for
performance of image fusion", Electronics Letters, Vol. 38, No. 7, 2002,
pp. 313-315.
[10] Z. Wang and A. C. Bovik, "A universal image quality index", IEEE
Signal Processing Letters, Vol. 9, No. 3, 2002, pp. 81-84.
[11] G. Piella and H. Heijmans, "A new quality metric for image fusion",
Proc. IEEE International Conference on Image Processing, Barcelona,
Spain, 2003, pp. 173-176
@article{"International Journal of Information, Control and Computer Sciences:50189", author = "Nedeljko Cvejic and Artur Łoza and David Bull and Nishan Canagarajah", title = "A Similarity Metric for Assessment of Image Fusion Algorithms", abstract = "In this paper, we present a novel objective nonreference
performance assessment algorithm for image fusion. It takes
into account local measurements to estimate how well the important
information in the source images is represented by the fused image.
The metric is based on the Universal Image Quality Index and uses
the similarity between blocks of pixels in the input images and the
fused image as the weighting factors for the metrics. Experimental
results confirm that the values of the proposed metrics correlate well
with the subjective quality of the fused images, giving a significant
improvement over standard measures based on mean squared error
and mutual information.", keywords = "Fusion performance measures, image fusion, nonreferencequality measures, objective quality measures.", volume = "2", number = "8", pages = "2592-5", }