63805
20190416220414.0
doi
10.1109/TSP.2007.894257
1053-587X
DAR
10940
ISI
000247488900012
ARTICLE
Super-Resolution from Unregistered and Totally Aliased Signals Using Subspace Methods
2007
Institute of Electrical and Electronics Engineers
2007
Journal Articles
In many applications, the sampling frequency is limited by the physical characteristics of the components: the pixel pitch, the rate of the A/D converter, etc. A low-pass filter is then often applied before the sampling operation to avoid aliasing. However, when multiple copies are available, it is possible to use the information that is inherently present in the aliasing to reconstruct a higher resolution signal. If the different copies have unknown relative offsets, this is a non-linear problem in the offsets and the signal coefficients. They are not easily separable in the set of equations describing the super-resolution problem. Thus, we perform joint registration and reconstruction from multiple unregistered sets of samples. We give a mathematical formulation for the problem when there are M sets of N samples of a signal that is described by L expansion coefficients. We prove that the solution of the registration and reconstruction problem is generically unique if MN>= L+M-1. We describe two subspace-based methods to compute this solution. Their complexity is analyzed, and some heuristic methods are proposed. Finally, some numerical simulation results on one and two-dimensional signals are given to show the performance of these methods.
aliasing
sampling
offset estimation
shift estimation
image registration
super-resolution
IVRG
NCCR-MICS/CL1
NCCR-MICS
241125
Vandewalle, Patrick
126649
244018
Sbaiz, Luciano
115222
Vandewalle, Joos
240184
Vetterli, Martin
107537
55
7, Part 2
3687-3703
IEEE Transactions on Signal Processing
http://lcav.epfl.ch/reproducible_research/VandewalleSVV06/
URL
1023270
http://infoscience.epfl.ch/record/63805/files/VandewalleSVV07.pdf
n/a
n/a
2930354
http://infoscience.epfl.ch/record/63805/files/VandewalleSVV07_code_rr.zip
252056
LCAV
U10434
252320
IVRL
U10429
oai:infoscience.tind.io:63805
IC
article
GLOBAL_SET
114218
114218
190838
LCAV-ARTICLE-2006-001
EPFL
REVIEWED
PUBLISHED
ARTICLE