IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved locality for irregular sampling algorithms
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
Temporal alignment of time varying MRI datasets for high resolution medical visualization
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Event Dynamics Based Temporal Registration
IEEE Transactions on Multimedia
IEEE Transactions on Image Processing
Choice of low resolution sample sets for efficient super-resolution signal reconstruction
Journal of Visual Communication and Image Representation
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Temporal registration refers to the methods used to align time varying sample sets with respect to each other. While reconstruction from a single sample set may generate aliasing, registration of multiple sample sets increases the effective sampling rate and therefore helps alleviate the problems created by low acquisition rates. However, since registration is mostly computed as an iterative best estimate, any error in registration translates directly into an increase in reconstruction error. In this paper we present a confidence measure based on local and global temporal registration errors, computed between sample sets, to determine if a given set of samples is suitable for inclusion in the reconstruction of a higher resolution temporal dataset. We also discuss implications of the non-uniform sampling theorem on the proposed confidence measure. Experimental results with real and synthetic data are provided to validate the proposed confidence measure.