Bayesian Image Super-Resolution
Abstract
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.
Cite
Text
Tipping and Bishop. "Bayesian Image Super-Resolution." Neural Information Processing Systems, 2002.Markdown
[Tipping and Bishop. "Bayesian Image Super-Resolution." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/tipping2002neurips-bayesian/)BibTeX
@inproceedings{tipping2002neurips-bayesian,
title = {{Bayesian Image Super-Resolution}},
author = {Tipping, Michael E. and Bishop, Christopher M.},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {1303-1310},
url = {https://mlanthology.org/neurips/2002/tipping2002neurips-bayesian/}
}