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Computational enhancement of large scale environmental imagery: aggregation of robust numerical regularization, neural computing and digital dynamic filtering

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dc.contributor.author Shkvarko, Yuriy
dc.contributor.author Villalón-Turrubiates, Iván E.
dc.date.accessioned 2016-04-04T21:15:06Z
dc.date.available 2016-04-04T21:15:06Z
dc.date.issued 2007
dc.identifier.citation 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. es
dc.identifier.issn 1742-7185
dc.identifier.uri http://hdl.handle.net/11117/3235
dc.description 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. es
dc.description.sponsorship CINVESTAV es
dc.language.iso eng es
dc.publisher International Journal of Computational Science and Engineering (IJCSE) es
dc.relation.ispartofseries IJCSE;3(3)
dc.rights.uri http://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdf es
dc.subject Nonlinear Regularisation es
dc.subject Image Enhancement es
dc.subject Numerical Inverse Problems es
dc.subject Entropy es
dc.subject Neural Networks es
dc.title Computational enhancement of large scale environmental imagery: aggregation of robust numerical regularization, neural computing and digital dynamic filtering es
dc.type info:eu-repo/semantics/article es
rei.revisor IJCSE
rei.peerreviewed Yes es


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