An MLP Neural Net with L1 and L2 Regularizers for Real Conditions of Deblurring. (14th September 2010)

• Record Type: Journal Article
• Authors: Santiago Santiago, Miguel A. Miguel A.; Cisneros Cisneros, Guillermo Guillermo; Bernués Bernués, Emiliano Emiliano
• Other Names: Capobianco Capobianco Enrico Enrico Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2010(2010)
• Publication Date: 2010-09-14
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2010/394615
• Languages: English
• ISSNs: 1687-6172

Abstract

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.