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Titolo:
Modeling for edge detection problems in blurred noisy images
Autore:
Bruni, C; De Santis, A; Iacoviello, D; Koch, G;
Indirizzi:
Univ Roma La Sapienza, Dipartimento Informat & Sistemist, I-00184 Rome, Italy Univ Roma La Sapienza Rome Italy I-00184 Sistemist, I-00184 Rome, Italy
Titolo Testata:
IEEE TRANSACTIONS ON IMAGE PROCESSING
fascicolo: 10, volume: 10, anno: 2001,
pagine: 1447 - 1453
SICI:
1057-7149(200110)10:10<1447:MFEDPI>2.0.ZU;2-H
Fonte:
ISI
Lingua:
ENG
Keywords:
edge detection; image reconstruction; multiscale processing; optimal estimation;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
24
Recensione:
Indirizzi per estratti:
Indirizzo: Bruni, C Univ Roma La Sapienza, Dipartimento Informat & Sistemist, I-00184Rome, Italy Univ Roma La Sapienza Rome Italy I-00184 t, I-00184 Rome, Italy
Citazione:
C. Bruni et al., "Modeling for edge detection problems in blurred noisy images", IEEE IM PR, 10(10), 2001, pp. 1447-1453

Abstract

The aim of this paper is to provide a theoretical set up and a mathematical model for the problem of image reconstruction. The original image belongsto a family of two-dimensional (2-D) possibly discontinuous functions, butis blurred by a Gaussian point spread function introduced by the measurement device. In addition, the blurred image is corrupted by an additive noise. We propose a preprocessing of data which enhances the contribution of thesignal discontinuous component over that one of the regular part, while damping down the effect of noise. In particular we suggest to convolute data with a kernel defined as the second order derivative of a Gaussian spread function. Finally, the image reconstruction is embedded in an optimal problem framework. Now convexity and compactness properties for the admissible set play a fundamental role. We provide an instance of a class of admissible sets which is relevant from an application point of view while featuring the desired properties.

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Documento generato il 26/01/20 alle ore 00:45:39