  Image registration is a crucial task in signal processing, but it often
encounters issues with stability and efficiency. Non-learning registration
approaches rely on optimizing similarity metrics between fixed and moving
images, which can be expensive in terms of time and space complexity. This
problem can be exacerbated when the images are large or there are significant
deformations between them. Recently, deep learning, specifically convolutional
neural network (CNN)-based methods, have been explored as an effective solution
to the weaknesses of non-learning approaches. To further advance learning
approaches in image registration, we introduce an attention mechanism in the
deformable image registration problem. Our proposed approach is based on a
Transformer framework called AiR, which can be efficiently trained on GPGPU
devices. We treat the image registration problem as a language translation task
and use the Transformer to learn the deformation field. The method learns an
unsupervised generated deformation map and is tested on two benchmark datasets.
In summary, our approach shows promising effectiveness in addressing stability
and efficiency issues in image registration tasks. The source code of AiR is
available on Github.
