Paper Info
Title
Super-resolution from highly undersampled images
Abstract
Aliasing artifacts in images are visually very disturbing. Therefore, most imaging devices apply a low-pass filter before sampling. This removes all aliasing from the image, but it also creates a blurred image. Actually, all the image information above half the sampling frequency is removed. In this paper, we present a new method for the reconstruction of a high resolution image from a set of highly undersampled and thus aliased images. We use the information in the entire frequency spectrum, including the aliased part, to create a sharp, high resolution image. The unknown relative shifts between the images are computed using a subspace projection approach. We show that the projection can be decomposed into multiple projections onto smaller subspaces. This allows for a considerable reduction of the overall computational complexity of the algorithm. A high resolution image can then be reconstructed from the registered low resolution images. Simulation results show the validity of our algorithm.
Year
DOI
Venue
2005
10.1109/ICIP.2005.1529894
Image Processing, 2005. ICIP 2005. IEEE International Conference
Keywords
Field
DocType
computational complexity,image reconstruction,image registration,image resolution,image sampling,low-pass filters,artifacts aliasing,blurred image,frequency spectrum,high resolution image reconstruction,highly undersampled images,low-pass filter,overall computational complexity,subspace projection approach,super-resolution
Iterative reconstruction,Computer vision,Subspace topology,Pattern recognition,Computer science,Sampling (signal processing),Aliasing,Low-pass filter,Artificial intelligence,Motion estimation,Image resolution,Image registration
Conference
Volume
ISSN
ISBN
1
1522-4880
0-7803-9134-9
Citations 
PageRank 
References 
11
0.99
2
Authors
4
Name
Order
Citations
PageRank
Patrick Vandewalle128420.24
Luciano Sbaiz28411.42
Martin Vetterli3139262397.68
Sabine Süsstrunk44984207.02