An Example-Based Two-Step Face Hallucination Method through Coefficient Learning
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Registration errors: are they always bad for super-resolution?
IEEE Transactions on Signal Processing
Hallucinating face by position-patch
Pattern Recognition
Performance of reconstruction-based super-resolution with regularization
Journal of Visual Communication and Image Representation
Hi-index | 35.69 |
Super resolution reconstruction of image sequences is highly dependent on quality of the motion estimation between successive frames. This work presents a statistical analysis of the least mean square (LMS) algorithm applied to super resolution reconstruction of an image sequence with translational global motion. Deterministic recursions are derived for the mean and mean square behaviors of the reconstruction error as functions of the registration errors. The new model describes the behavior of the algorithm in realistic situations, and significantly improves the accuracy of a simple model available in the literature. Monte Carlo simulations show very good agreement between actual and predicted behaviors