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.

翻译：图像配准传统上采用两种截然不同的方法：基于学习的方法依赖强大的深度神经网络，而基于优化的方法则应用复杂的数学变换来扭曲图像。当然，这两种范式各有优劣，在本研究中，我们试图将各自的优势整合到一个统一的框架中，将基于学习方法的输出作为优化的初始参数，同时将计算资源优先分配给损失最大的图像对。我们的研究显示，在保持相同推理时间的前提下，测试数据性能提升高达1.6%，变形场平滑度指标显著提高1.0个百分点。