Truncated edges estimation using MLP neural nets applied to regularized image restoration

Bernués, Emiliano; Cisneros, Guillermo; Capella, Matthew

doi:10.1109/icip.2002.1038030

Cited by 1 publication

(3 citation statements)

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“…In fact, the Hopfield network has been used in the literature to solve the optimization problem (9) and recent studies provide neural network solutions to the TV regularization (10) as in [16,17]. In this article, we present a simple solution to solve the TV-based solution by means of an MLP with back-propagation.…”

Section: Problem Formulationmentioning

confidence: 99%

“…A first step of our neural net was given in a previous study [9] using the standard l 2 norm in the energy function, as done in other regularization algorithms [11][12][13][14][15]. However, the success of the total variation (TV) in deconvolution [16][17][18][19][20] motivated its incorporation in the MLP.…”

Section: Contributionmentioning

confidence: 99%

“…In recent studies [9,10], we have developed an algorithm using a multilayer perceptron (MLP) to restore a real image without relying on the typical BCs of the literature. The main goal is to model the blurred image as truncation of the convolution operator, where the boundaries have been removed and they are not further used in the algorithm.…”

Section: Contributionmentioning

confidence: 99%

See 2 more Smart Citations

Boundary reconstruction process of a TV-based neural net without prior conditions

Santiago

Cisneros

Bernués

2011

EURASIP J. Adv. Signal Process.

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Image restoration aims to restore an image within a given domain from a blurred and noisy acquisition. However, the convolution operator, which models the degradation, is truncated in a real observation causing significant artifacts in the restored results. Typically, some assumptions are made about the boundary conditions (BCs) outside the field of view to reduce the ringing. We propose instead a restoration method without prior conditions which reconstructs the boundary region as well as making the ringing artifact negligible. The algorithm of this article is based on a multilayer perceptron (MLP) which minimizes a truncated version of the total variation regularizer using a back-propagation strategy. Various experiments demonstrate the novelty of the MLP in the boundary restoration process without neither any image information nor prior assumption on the BCs.

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Section: Problem Formulationmentioning

confidence: 99%