Nonlinear model identification and seethrough cancellation from recto-verso data (Note interne, manuali e guide)

Type
Label
  • Nonlinear model identification and seethrough cancellation from recto-verso data (Note interne, manuali e guide) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Alternative label
  • Salerno Emanuele, Martinelli Francesca, Tonazzini Anna (2011)
    Nonlinear model identification and seethrough cancellation from recto-verso data
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Salerno Emanuele, Martinelli Francesca, Tonazzini Anna (literal)
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  • CODICE PUMA: /cnr.isti/2011-B4-004; DISCIPLINA DI RIFERIMENTO: Computer Science & Engineering; AREA DI VALUTAZIONE: 01 - Scienze matematiche e informatiche; PROGETTO: Acquisizione Multispettrale, Miglioramento, Indicizzazione e Ricerca di Artefatti Artistici (literal)
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  • Altro (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR ISTI (literal)
Titolo
  • Nonlinear model identification and seethrough cancellation from recto-verso data (literal)
Abstract
  • The problem of seethrough cancellation in digital images of double-sided documents is addressed. Previous approaches to solve this problem from recto-verso pairs of grayscale data images show a number of drawbacks, ranging from errors due to an inadequate data model to excessive computational complexities. While satisfying the need to assume a nonlinear convolutional mixture model and to estimate its parameters along with the recto and verso patterns, we propose a simple and fast strategy to estimate the trasparency of the paper and the seethrough convolutional kernel, thus enabling an efficient correction of this distortion. Compared to other separation strategies, our choice is slightly more cumbersome since average background values must be estimated and a pure showthrough area must be isolated manually by the operator. Although the procedure cannot be fully automatic, however, it outperforms other restoration strategies, especially if based on linear instantaneous models. (literal)
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