Abstract
The Hermite transform is introduced as an image representation model for multiresolution image fusion with noise reduction. Image fusion is achieved by combining the steered Hermite coefficients of the source images, then the coefficients are combined with a decision rule based on the linear algebra through a measurement of the linear dependence. The proposed algorithm has been tested on both multi-focus and multi-modal image sets producing results that exceed results achieved with other methods such as wavelets, curvelets [11], and contourlets [2] proving that our scheme best characterized important structures of the images at the same time that the noise was reduced.
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Cruz-Techica, S., Escalante-Ramirez, B. (2011). Image Fusion Algorithm Using the Multiresolution Directional-Oriented Hermite Transform. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Ben-Youssef Brants, C., Hancock, E.R. (eds) Pattern Recognition. MCPR 2011. Lecture Notes in Computer Science, vol 6718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21587-2_22
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DOI: https://doi.org/10.1007/978-3-642-21587-2_22
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