An error-weighted regularization algorithm for image motion-field estimation

Local motion measurement errors are used to guide the global smoothing process in order to preserve motion-field discontinuities. A field-smoothing algorithm based on matching-error weighting is proposed. The added computation is minimal, since it uses byproducts of the local measurement process. The error-weighting functional provides significantly improved motion field estimates, as measured by motion-compensated interpolation performance. However, the mean-square reconstruction error is somewhat higher than that obtained by performing the much more computationally expensive stochastic optimization