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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Robust Super-Resolution Using a Median Filter for Irregular Samples
verfasst von : Alfonso Sánchez-Beato, Gonzalo Pajares
Erschienen in: Pattern Recognition and Image Analysis
Verlag: Springer Berlin Heidelberg
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Super-resolution (SR) techniques produce a high resolution (HR) image from a set of low-resolution (LR) undersampled images. Usually, SR problems are posed as estimation problems where the LR images are contaminated by stationary noise. However, in real SR problems is very common to have non-stationary noise due to problems in the registration of the images or outliers. SR methods that address this type of problems are called robust. In this paper we propose a novel robust SR method that employs a median filter directly in the data from the LR images, before proceeding to the interpolation and deblurring steps that are common in SR. We compare this new method with other robust SR methods with synthetic and real data, proving that it outperforms the other methods in both cases.