Bayesian Image Super-Resolution

The extraction of a single high-quality image from a set of low(cid: 173) resolution images is an important problem which arises in fields such as remote sensing, surveillance, medical imaging and the ex(cid: 173) traction of still images from video. Typical approaches are based on the use of cross-correlation to register the images followed by the inversion of the transformation from the unknown high reso(cid: 173) lution image to the observed low resolution images, using regular(cid: 173) ization to resolve the ill-posed nature of the inversion process. In this paper we develop a Bayesian treatment of the super-resolution problem in which the likelihood function for the image registra(cid: 173) tion parameters is based on a marginalization over the unknown high-resolution image. This approach allows us to estimate the unknown point spread function, and is rendered tractable through the introduction of a Gaussian process prior over images. Results indicate a significant improvement over techniques based on MAP (maximum a-posteriori) point optimization of the high resolution image and associated registration parameters.

Authors

• Michael Tipping
• Christopher Bishop

Keywords

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