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Ancient documents are usually degraded by the presence of strong background artifacts. These are often caused by the so-called bleed-through effect, a pattern that interferes with the main text due to seeping of ink from the reverse side. A similar effect, called show-through and due to the nonperfect opacity of the paper, may appear in scans of even(More)
In this paper, we apply Bayesian blind source separation (BSS) from noisy convolutive mixtures to jointly separate and restore source images degraded through unknown blur operators, and then linearly mixed. We found that this problem arises in several image processing applications, among which there are some interesting instances of degraded document(More)
We propose a novel approach to restoring digital document images, with the aim of improving text legibility and OCR performance. These are often compromised by the presence of artifacts in the background, derived from many kinds of degradations , such as spots, underwritings, and show-through or bleed-through effects. So far, background removal techniques(More)
Astrophysical radiation maps provide images which are superpositions of various cosmological components such as the cosmic microwave background (CMB) radiation, galactic dust, synchrotron, free-free emission and extragalactic radio sources. All these components are of great interest to cosmologists and in particular CMB, in addition to being the picture of(More)
Blind Source Separation techniques, based both on Independent Component Analysis and on second order statistics, are presented and compared for extracting partially hidden texts and textures in document images. Barely perceivable features may occur, for instance, in ancient documents previously erased and then rewritten (palimpsests), or for transparency or(More)
This paper deals with blind separation of images from noisy linear mixtures with unknown coefficients, formulated as a Bayesian estimation problem. This is a flexible framework, where any kind of prior knowledge about the source images and the mixing matrix can be accounted for. In particular, we describe local correlation within the individual images(More)