Subspace-based methods for image registration and super-resolution

Super-resolution algorithms combine multiple low resolution images into a single high resolution image. They have received a lot of attention recently in various application domains such as HDTV, satellite imaging, and video surveillance. These techniques take advantage of the aliasing present in th...

Full description

Saved in:
Bibliographic Details
Published in2008 15th IEEE International Conference on Image Processing pp. 645 - 648
Main Authors Vandewalle, P., Baboulaz, L., Dragotti, P.L., Vetterli, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2008
Subjects
Frequency
HDTV
High-resolution imaging
Image reconstruction
Image registration
Image resolution
Image restoration
Image sampling
Satellites
Signal resolution
Spectral analysis
Spline functions
Video surveillance
Online AccessGet full text

Cover

More Information
Summary:Super-resolution algorithms combine multiple low resolution images into a single high resolution image. They have received a lot of attention recently in various application domains such as HDTV, satellite imaging, and video surveillance. These techniques take advantage of the aliasing present in the input images to reconstruct high frequency information of the resulting image. One of the major challenges in such algorithms is a good alignment of the input images: subpixel precision is required to enable accurate reconstruction. In this paper, we give an overview of some subspace techniques that address this problem. We first formulate super-resolution in a multichannel sampling framework with unknown offsets. Then, we present three registration methods: one approach using ideas from variable projections, one using a Fourier description of the aliased signals, and one using a spline description of the sampling kernel. The performance of the algorithms is evaluated in numerical simulations.
ISBN:9781424417650
1424417651
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2008.4711837