Sparse image super-resolution via superset selection and pruning
Name
Demanet_Sparse image.pdf
Size
231.21 KB
Format
Adobe PDF
Checksum (MD5)
a39d59f22f96929e29380e12850adf8b
Author(s)
Demanet, Laurent
Nguyen, Nam Hoai
Date Issued
December 2013
Journal
Proceedings of the 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Nguyen, Nam, and Laurent Demanet. “Sparse Image Super-Resolution via Superset Selection and Pruning.” 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) (December 2013).
Version
Author's final manuscript
Abstract
This note extends the superset method for sparse signal recovery from bandlimited measurements to the two-dimensional case. The algorithm leverages translation-invariance of the Fourier basis functions by constructing a Hankel tensor, and identifying the signal subspace from its range space. In the noisy case, this method determines a superset which then needs to undergo pruning. The method displays reasonable robustness to noise, and unlike ℓ [subscript 1] minimization, always succeeds in the noiseless case.
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CAMSAP.2013.6714044
