Abstract
We propose a Bayesian image superresolution method that estimates a high-resolution background image from a sequence of occluded observations. We assume that the occlusions have spatial and temporal continuities. Such assumptions would be plausible, for example, when satellite images are occluded by clouds or when a tourist site is obstructed by people. Although the exact inference of our model is difficult, an efficient superresolution algorithm is derived by using a variational Bayes technique. Experiments show that our superresolution method performs better than existing methods that do not assume the occlusions or that assume the occlusions but do not assume the temporal continuities of the occlusions.
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© 2009 Springer-Verlag Berlin Heidelberg
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Fukuda, W., Kanemura, A., Maeda, Si., Ishii, S. (2009). Superresolution from Occluded Scenes. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_3
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DOI: https://doi.org/10.1007/978-3-642-10684-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10682-8
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