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Open Access

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

  • Miguel A. Santiago1Email author,
  • Guillermo Cisneros1 and
  • Emiliano Bernués2
EURASIP Journal on Advances in Signal Processing20102010:394615

Received: 19 March 2010

Accepted: 6 September 2010

Published: 14 September 2010


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.


Boundary ConditionInformation TechnologyDeblurringQuantum InformationReal Model

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Authors’ Affiliations

Departamento de Señales, Sistemas y Radiocomunicaciones, Escuela Téchica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
Departamento de Ingeniería Electrónica y Comunicaciones, Centro Politécnico Superior, Universidad de Zaragoza, Zaragoza, Spain


© Miguel A. Santiago et al. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.