  Image registration has traditionally been done using two distinct approaches:
learning based methods, relying on robust deep neural networks, and
optimization-based methods, applying complex mathematical transformations to
warp images accordingly. Of course, both paradigms offer advantages and
disadvantages, and, in this work, we seek to combine their respective strengths
into a single streamlined framework, using the outputs of the learning based
method as initial parameters for optimization while prioritizing computational
power for the image pairs that offer the greatest loss. Our investigations
showed improvements of up to 1.6% in test data, while maintaining the same
inference time, and a substantial 1.0% points performance gain in deformation
field smoothness.
