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An MLP Neural Net with L1 and L2 Regularizers for Real Conditions of Deblurring

Abstract

Real conditions of deblurring involve a spatially nonlinear process since the borders are truncated, causing significant artifacts in the restored results. Typically, it is assumed to have boundary conditions to reduce ringing; in contrast, this paper proposes a restoration method which simply deals with null borders. We minimize a deterministic regularized function in a Multilayer Perceptron (MLP) with no training and follow a back-propagation algorithm with the L1 and L2 norm-based regularizers. As a result, the truncated borders are regenerated while adapting the center of the image to the optimum linear solution. We report experimental results showing the good performance of our approach in a real model without borders. Even if using boundary conditions, the quality of restoration is comparable to other recent researches.

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Correspondence to Miguel A. Santiago.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Santiago, M.A., Cisneros, G. & Bernués, E. An MLP Neural Net with L1 and L2 Regularizers for Real Conditions of Deblurring. EURASIP J. Adv. Signal Process. 2010, 394615 (2010). https://doi.org/10.1155/2010/394615

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Keywords

  • Boundary Condition
  • Information Technology
  • Deblurring
  • Quantum Information
  • Real Model