A pel-recursive Wiener-based displacement estimation algorithm
Signal Processing
Image restoration using reduced order models
Signal Processing - Multidimensional Signal Processing, Part II
Bayesian Estimation of Motion Vector Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Restoration
Enhancing video denoising algorithms by fusion from multiple views
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Hi-index | 0.00 |
In this paper, we develop a recursive model-based maximum a posteriori (MAP) estimator that simultaneously estimates the displacement vector field (DVF) and intensity field from a noisy-blurred image sequence. Current motion-compensated spatio-temporal filters treat the estimation of the DVF as a preprocessing step. Thus, no attempt is made to verify the accuracy of these estimates prior to their use in the filter. By simultaneously estimating these two fields, information is made available to each filter regarding the reliability of estimates that they are dependent upon. Nonstationary models are used for the DVF and the intensity field in the proposed estimator, thus avoiding the smoothing of boundaries present in both.