NDLI: Joint blind separation and restoration of mixed degraded images for document analysis

Content Provider	IEEE Xplore Digital Library
Author	Tonazzini, A. Gerace, I. Cricco, F.
Copyright Year	2004
Description	Author affiliation: Ist. di Scienza a Tecnologie dell'Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy (Tonazzini, A.)
Abstract	We consider the problem of extracting clean images from noisy mixtures of images degraded by blur operators. This special case of source separation arises, for instance, when analyzing document images showing bleed-through or show-through. We propose to jointly perform demixing and deblurring by augmenting blind source separation with a step of image restoration. Within the independent component analysis (ICA) approach, i.e. assuming the statistical independence of the sources, we adopt a Bayesian formulation where the priors on the ideal images are given in the form of Markov random field (MRF), and a MAP estimation is employed for the joint recovery of the mixing matrix and the images. We show that taking into account the blur model and a proper image model improves the separation process and makes it more robust against noise. Preliminary results on synthetic examples of documents exhibiting bleed-through are provided, considering edge-preserving priors that are suitable to describe text images.
Starting Page	311
Ending Page	314
File Size	324614
Page Count	4
File Format	PDF
ISBN	0780385543
ISSN	15224880
DOI	10.1109/ICIP.2004.1418752
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-10-24
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image restoration Degradation Text analysis Image analysis Independent component analysis Source separation Blind source separation Bayesian methods Markov random fields Separation processes
Content Type	Text
Resource Type	Article

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