Gradient based nonuniform subsampling for information-theoretic alignment methods.

We examine the computation of information-theoretic image registration metrics and propose two (deterministic and stochastic) nonuniform subsampling methods for improving the efficiency. The proposed schemes attempt to use only the most relevant information as the basis of the computation. Both methods are shown to yield considerable improvement over the current practice of uniform subsampling. Theoretical and experimental evidence is provided.