  Remote sensing image registration is valuable for image-based navigation
system despite posing many challenges. As the search space of registration is
usually non-convex, the optimization algorithm, which aims to search the best
transformation parameters, is a challenging step. Conventional optimization
algorithms can hardly reconcile the contradiction of simultaneous rapid
convergence and the global optimization. In this paper, a novel learning-based
optimization algorithm named Image Registration Optimizer Network (IRON) is
proposed, which can predict the global optimum after single iteration. The IRON
is trained by a 3D tensor (9x9x9), which consists of similar metric values. The
elements of the 3D tensor correspond to the 9x9x9 neighbors of the initial
parameters in the search space. Then, the tensor's label is a vector that
points to the global optimal parameters from the initial parameters. Because of
the special architecture, the IRON could predict the global optimum directly
for any initialization. The experimental results demonstrate that the proposed
algorithm performs better than other classical optimization algorithms as it
has higher accuracy, lower root of mean square error (RMSE), and more
efficiency. Our IRON codes are available for further
study.https://www.github.com/jaxwangkd04/IRON
