Data Fusion BAUE Estimation of a deterministic vector, applications to image noise and blur reduction
In this work we conceive centralized data fusion as a deterministic parameter estimation problem. Two different criterions are compared: best affine unbiased fusion rule (BAUE), and Maximum Likelihood for Gaussian measurement noise. Estimates are described in terms of their covariance matrices, the...
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| Autores principales: | , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2010
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/153582 http://39jaiio.sadio.org.ar/sites/default/files/39-jaiio-ast-03.pdf |
| Aporte de: |
| Sumario: | In this work we conceive centralized data fusion as a deterministic parameter estimation problem. Two different criterions are compared: best affine unbiased fusion rule (BAUE), and Maximum Likelihood for Gaussian measurement noise. Estimates are described in terms of their covariance matrices, the Cramer-Rao lower bound and simulations. The developed fusion rules are suited to two different image fusion cases: noise reduction under differently exposed images, and blur reduction based on lens response knowledge. |
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