We propose a new optimization model for non-rigid registration of images using multi-metrics. The ordinary searching
step of optimization has been often trapped in local minima and produces wrong registration results. In this paper, if the
condition occurs, multi-metrics model will switch to the other metrics to get rid of the local minima, vice versa, until
optimization cannot proceed any more for any of the metrics. We have tested our approach in a variety of experimental
conditions and compared the results with the optimization without multi-metrics. The results indicate that the new model
is robust and fast in non-rigid registration.