We describe an image fusion algorithm for data exhibiting heavy tails with no convergent second- or higher-order moments. Our developments rely on recent results showing that wavelet decomposition coefficients of images are best modeled by alpha-stable distributions, a family of heavy-tailed densities. Thus, in the multiscale wavelet domain we develop a novel fusion rule based on fractional lower order moments (FLOM's). Simulation results show that our method achieves better performance in comparison with previously proposed pixel-level fusion approaches.
|Translated title of the contribution||Complex wavelet domain image fusion based on fractional lower order moments|
|Title of host publication||IEEE 8th International Conference on Information Fusion, Philadelphia, USA|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Pages||515 - 521|
|Number of pages||7|
|Publication status||Published - Jul 2005|
|Event||8th International Conference on Information Fusion - Philadelphia, PA, United States|
Duration: 1 Jul 2005 → …
|Conference||8th International Conference on Information Fusion|
|Period||1/07/05 → …|
Bibliographical noteConference Proceedings/Title of Journal: IEEE 8th International Conference on Information Fusion
Rose publication type: Conference contribution
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- image fusion
- wavelet decomposition
- alpha-stable distributions
- parameter estimation
- fractional lower order moments
- symmetric covariation