Recently, a multi-sensor image fusion system has been widely investigated due to its growing applications. In the
system, robust and accurate multi-modal image registration is essential and the fast registration is also important for
many applications. In this paper, we propose a fast algorithm for registering multi-modal images that are acquired from
two different sensors: electro-optic (EO) and infrared (IR). In the registration of multi-modal images, a normalized
mutual information (NMI) based registration algorithm is preferred due to its robust and accurate performance. And the
downhill simplex optimization scheme is popular in NMI-based registration because of its fast convergence rate.
However, since it still suffers from a high computational complexity, the complexity should be reduced further for (semi-
) real-time applications. In this paper, we attempt to reduce the computational complexity in the registration process. We
first modify the searching methodology for unconstrained function minimization in the ordinary downhill simplex
algorithm, by suggesting new vertex movements for fast vertex contraction. Thereby, we can reduce the number of
function evaluations. We also minimize the function evaluation time by linearizing the projective transformation in the
interpolation routine. Simulation results show that the proposed algorithm noticeably reduces the computational
complexity by 30% compared to the conventional NMI-based registration algorithm.