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Multidimensional Signal Interpolation Based on Factorization and Dimension Reduction of Decision Rules

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Abstract

We research adaptive multidimensional signal interpolators based on switching between several interpolating functions at each signal sample. We perform the switching by decision rule, which is optimized for each signal in the parameter space of this decision rule. Algorithms for factorization and dimension reduction of decision rules are proposed. We investigate new classes of interpolating functions and systems of local features. We propose fitting procedures for adaptive interpolators. We perform the software implementation of the developed algorithms. A numerical experiment in natural multidimensional signals (video, remote sensing data and hyperspectral data) confirms the gain of the adaptive interpolator.

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Funding

The work was partly funded:

by RFBR according to the research project 18-01-00667 in parts of “1 Interpolator decision rule parameterization”—“8 Experimental study of the adaptive interpolator”;

by the Russian Federation Ministry of Science and Higher Education within a state contract with the “Crystallography and Photonics” Research Center of the RAS under agreement 007-ГЗ/Ч3363/26 in part of “Introduction”.

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Authors and Affiliations

  1. Samara National Research University, 443086, Samara, Russia

    M. V. Gashnikov

  2. IPSI RAS—Branch of the FSRC “Crystallography and Photonics,” Russian Academy of Sciences, 443001, Samara, Russia

    M. V. Gashnikov

Authors
  1. M. V. Gashnikov
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Correspondence to M. V. Gashnikov.

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Gashnikov, M.V. Multidimensional Signal Interpolation Based on Factorization and Dimension Reduction of Decision Rules. Opt. Mem. Neural Networks 28, 332–342 (2019). https://doi.org/10.3103/S1060992X1904009X

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  • DOI: https://doi.org/10.3103/S1060992X1904009X

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