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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1182

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contributor.authorMishra, S K-
contributor.authorPanda, G-
contributor.authorMeher, S-
contributor.authorSahoo, A K-
date.accessioned2010-02-22T06:23:30Z-
date.available2010-02-22T06:23:30Z-
date.issued2009-
identifier.citationInternational Conference on Advanced Computer Control, 2009, pp.355-359en
identifier.urihttp://doi.ieeecomputersociety.org/10.1109/ICACC.2009.120-
identifier.urihttp://hdl.handle.net/2080/1182-
description.abstractHere we have presented an alternate ANN structure called functional link ANN (FLANN) for image denoising. In contrast to a feed forward ANN structure i.e. a multilayer perceptron (MLP), the FLANN is basically a single layer structure in which non-linearity is introduced by enhancing the input pattern with nonlinear function expansion. In this work three different expansions is applied. With the proper choice of functional expansion in a FLANN , this network performs as good as and in some case even better than the MLP structure for the problem of denoising of an image corrupted with Gaussian noise. In the single layer functional link ANN (FLANN) the need of hidden layer is eliminated. The novelty of this structure is that it requires much less computation than that of MLP. In the presence of additive white Gaussian noise in the image, the performance of the proposed network is found superior to that of a MLP .In particular FLANN structure with exponential function expansion works best for Gaussian noise suppression from an imageen
format.extent516417 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherIEEE Computer Societyen
titleExponential Functional Link Artificial Neural Networks for Denoising of Image Corrupted by Gaussian Noiseen
typeArticleen
Appears in Collections:Conference Papers

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