Computational enhancement of large scale environmental imagery: aggregation of robust numerical regularization, neural computing and digital dynamic filtering
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Fecha
2007
Autores
Shkvarko, Yuriy
Villalón-Turrubiates, Iván E.
Título de la revista
ISSN de la revista
Título del volumen
Editor
International Journal of Computational Science and Engineering (IJCSE)
Resumen
Descripción
We address a new efficient robust optimisation approach to large-scale environmental image reconstruction/enhancement as required for remote sensing imaging with multi-spectral array sensors/SAR. First, the problem-oriented robustification of the previously proposed Fused Bayesian-Regularization (FBR) enhanced imaging method is performed to alleviate its ill-poseness due to system-level and model-model uncertainties. Second, the modification of the Hopfield-type Maximum Entropy Neural Network (MENN) is proposed that enables such MENN to perform numerically the robustified FBR technique via computationally efficient iterative scheme. The efficiency of the aggregated robust regularised MENN technique is verified through simulation studies of enhancement of the real-world environmental images.
Palabras clave
Nonlinear Regularisation, Image Enhancement, Numerical Inverse Problems, Entropy, Neural Networks
Citación
Y. Shkvarko & I.E. Villalón-Turrubiates (2007). “Computational enhancement of large scale environmental imagery: aggregation of robust numerical regularization, neural computing and digital dynamic filtering”, International Journal of Computational Science and Engineering (IJCSE), 3(3), pp.219-231.