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A statistical approach to the problem of restoring damaged and
contaminated images
by Richard Everitt and Richard Glendinning
We address the problem of automatically identifying and restoring damaged
and contaminated images. We suggest a novel approach based on a
semi-parametric model. This has two components, a parametric component
describing known physical characteristics and a more flexible
non-parametric component. The latter avoids the need for a detailed model
for the sensor, which is often costly to produce and lacking in
robustness. We assess our approach using an analysis of
electroencephalographic images contaminated by eye-blink artefacts and
highly damaged photographs contaminated by non-uniform lighting. These
experiments show that our approach provides an effective solution to
problems of this type.
Key words: Bayesian statistics; Damaged images; EEG artefacts; Illumination
variations; Photographs; Semi-parametric model.
Full text of the paper (pdf),
which was published in Pattern Recognition, 2009.
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