Sparse image super-resolution via superset selection and pruning
Author(s)Demanet, Laurent; Nguyen, Nam Hoai
MetadataShow full item record
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.
DepartmentMassachusetts Institute of Technology. Department of Mathematics
Proceedings of the 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Institute of Electrical and Electronics Engineers (IEEE)
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).
Author's final manuscript