Bayesian Image Super-Resolution

The extraction of a single high-quality image from a set of low-resolution images is an important problem which arises in fields such as remote sensing, surveillance, medical imaging and the extraction 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 resolution image to the observed low resolution images, using regularization 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 registration parameters is based on a marginalization over the unknown high-re solution 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.

[1]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[2]  Takeo Kanade,et al.  Limits on super-resolution and how to break them , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[3]  Andrew Zisserman,et al.  Super-resolution enhancement of text image sequences , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[4]  Peyman Milanfar,et al.  A computationally efficient superresolution image reconstruction algorithm , 2001, IEEE Trans. Image Process..

[5]  Russell C. Hardie,et al.  Joint MAP registration and high-resolution image estimation using a sequence of undersampled images , 1997, IEEE Trans. Image Process..

[6]  Andrew Blake,et al.  Motion Deblurring and Super-resolution from an Image Sequence , 1996, ECCV.

[7]  David A. Maluf,et al.  Bayesian super-resolved surface reconstruction from images , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[8]  Ian T. Nabney,et al.  Netlab: Algorithms for Pattern Recognition , 2002 .