Superresolution from Occluded Scenes

  • Authors:

  • Wataru Fukuda;Atsunori Kanemura;Shin-Ich Maeda;Shin Ishii

  • Affiliations:

  • Graduate School of Informatics, Kyoto University, Kyoto, Japan 611-0011;Graduate School of Informatics, Kyoto University, Kyoto, Japan 611-0011;Graduate School of Informatics, Kyoto University, Kyoto, Japan 611-0011;Graduate School of Informatics, Kyoto University, Kyoto, Japan 611-0011

  • Venue:
  • ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
  • Year:
  • 2009

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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.