TY - GEN
T1 - A general sparse image prior combination in super-resolution
AU - Villena, Salvador
AU - Vega, Miguel
AU - Molina, Rafael
AU - Katsaggelos, Aggelos K.
PY - 2013
Y1 - 2013
N2 - In this paper the Super-Resolution (SR) image registration and reconstruction problem is studied within the Bayesian framework using a general sparse image prior combination. The representation of the proposed priors as Scale Mixtures of Gaussians (SMG), leads to the introduction of variational parameters, for which degenerate distributions are assumed. In the proposed method all the problem unknowns are automatically estimated using variational techniques. An experimental comparison between the proposed and state of the art methods has been performed, on both synthetic and real images.
AB - In this paper the Super-Resolution (SR) image registration and reconstruction problem is studied within the Bayesian framework using a general sparse image prior combination. The representation of the proposed priors as Scale Mixtures of Gaussians (SMG), leads to the introduction of variational parameters, for which degenerate distributions are assumed. In the proposed method all the problem unknowns are automatically estimated using variational techniques. An experimental comparison between the proposed and state of the art methods has been performed, on both synthetic and real images.
KW - Image processing
KW - Superresolution
UR - http://www.scopus.com/inward/record.url?scp=84888871335&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84888871335&partnerID=8YFLogxK
U2 - 10.1109/ICDSP.2013.6622841
DO - 10.1109/ICDSP.2013.6622841
M3 - Conference contribution
AN - SCOPUS:84888871335
SN - 9781467358057
T3 - 2013 18th International Conference on Digital Signal Processing, DSP 2013
BT - 2013 18th International Conference on Digital Signal Processing, DSP 2013
T2 - 2013 18th International Conference on Digital Signal Processing, DSP 2013
Y2 - 1 July 2013 through 3 July 2013
ER -