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Image Fusion for Enhanced Visualization: A Variational Approach

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Abstract

We present a variational model to perform the fusion of an arbitrary number of images while preserving the salient information and enhancing the contrast for visualization. We propose to use the structure tensor to simultaneously describe the geometry of all the inputs. The basic idea is that the fused image should have a structure tensor which approximates the structure tensor obtained from the multiple inputs. At the same time, the fused image should appear ‘natural’ and ‘sharp’ to a human interpreter. We therefore propose to combine the geometry merging of the inputs with perceptual enhancement and intensity correction. This is performed through a minimization functional approach which implicitly takes into account a set of human vision characteristics.

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References

  • Aggarval, J. K. (1993). Multisensor fusion for computer vision. Berlin: Springer.

    Google Scholar 

  • Bertalmío, M., Caselles, V., Provenzi, E., & Rizzi, A. (2007). Perceptual color correction through variational techniques. IEEE Transactions on Image Processing, 16(4), 1058–1072.

    Article  MathSciNet  Google Scholar 

  • Burt, P. J. (1984). The pyramid as a structure for efficient computation. In A. Rosenfeld (Ed.), Multiresolution image processing and analysis (pp. 6–35). Berlin: Springer.

    Google Scholar 

  • Burt, P. J., & Kolczynski, R. J. (1993). Enhanced image capture through fusion. In Proceedings of the 4th international conference on computer vision (pp. 173–182). Berlin, Germany, May.

  • Cumani, A. (1991). Edge detection in multispectral images. CVGIP: Graphical Models and Image Processing, 53(1), 40–51.

    Article  MATH  Google Scholar 

  • Di Zenzo, S. (1986). A note on the gradient of multi-image. Computer Vision, Graphics, and Image Processing, 33, 116–125.

    Article  Google Scholar 

  • Haber, E., & Modersitzki, J. (2007). Intensity gradient based registration and fusion of multi-modal images. In Methods of information in medicine (pp. 726–733). Stuttgart: Schattauer.

    Google Scholar 

  • Harikumar, G., & Bresler, Y. (1996). Feature extraction for exploratory visualization of vector valued imagery. IEEE Transactions on Image Processing, 5(9), 1324–1334.

    Article  Google Scholar 

  • Hill, D., Edwards, P., & Hawkes, D. (1994). Fusing medical images. Image Processing, 6(2), 22–24.

    Google Scholar 

  • Jost, J. (2002). Riemannian geometry and geometric analysis. Berlin: Springer.

    MATH  Google Scholar 

  • Li, H., Manjunath, B. S., & Mitra, S. K. (1995). Multisensor image fusion using the wavelet transform. Graphical Models and Image Processing, 57(3), 235–245.

    Article  Google Scholar 

  • Manduca, A. (1996). Multispectral image visualization with nonlinear projections. IEEE Transactions on Image Processing, 5(10), 1486–1490.

    Article  Google Scholar 

  • Petrovic, V. S. (2007). Subjective tests for image fusion evaluation and objective metric validation. Information Fusion, 8(2), 208–216.

    Article  MathSciNet  Google Scholar 

  • Piella, G. (2003). A general framework for multiresolution image fusion: from pixels to regions. Information Fusion, 9, 259–280.

    Article  Google Scholar 

  • Piella, G., & Heijmans, H. J. A. M. (2003). A new quality metric for image fusion. Proceedings of the IEEE International Conference on Image Processing, 2, 173–176. Barcelona, Spain, September 14–17.

    Google Scholar 

  • Qu, G. H., Zhang, D. L., & Yan, P. F. (2001). Medical image fusion by wavelet transform modulus maxima. Journal of the Optical Society of America, 9(4), 184–190.

    Google Scholar 

  • Rogers, S. K., Wilson, T. A., & Kabrisky, M. (1997). Perceptual-based image for hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 35(4), 1007–1017.

    Article  Google Scholar 

  • Scheunders, P., & De Backer, S. (2001). Fusion and merging of multispectral images using multiscale fundamental forms. Journal of the Optical Society of America A, 18(10), 2468–2477.

    Article  Google Scholar 

  • Socolinsky, D. A., & Wolff, L. B. (2002). Multispectral image visualization through first-order fusion. IEEE Transactions on Image Processing, 11(8), 923–931.

    Article  Google Scholar 

  • Wang, Z., Bovik, A., Sheikh, H., & Simoncelli, E. (2004). Image quality assessment: From error measurement to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–613.

    Article  Google Scholar 

  • Weickert, J. (1998). Anisotropic diffusion in image processing. Stuttgart: Teubner.

    MATH  Google Scholar 

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Authors and Affiliations

  1. Pompeu Fabra University, Barcelona, Spain

    Gemma Piella

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  1. Gemma Piella
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Correspondence to Gemma Piella.

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Piella, G. Image Fusion for Enhanced Visualization: A Variational Approach. Int J Comput Vis 83, 1–11 (2009). https://doi.org/10.1007/s11263-009-0206-4

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  • DOI: https://doi.org/10.1007/s11263-009-0206-4

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