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  • Research Article
  • Open Access

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

  • 1Email author,
  • 1 and
  • 2
EURASIP Journal on Advances in Signal Processing20102010:394615

https://doi.org/10.1155/2010/394615

  • Received: 19 March 2010
  • Accepted: 6 September 2010
  • Published:

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.

Keywords

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

Publisher note

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Authors’ Affiliations

(1)
Departamento de Señales, Sistemas y Radiocomunicaciones, Escuela Téchica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, Spain
(2)
Departamento de Ingeniería Electrónica y Comunicaciones, Centro Politécnico Superior, Universidad de Zaragoza, 50018 Zaragoza, Spain

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