  Image registration is an essential process for aligning features of interest
from multiple images. With the recent development of deep learning techniques,
image registration approaches have advanced to a new level. In this work, we
present 'Rotation-Equivariant network and Transformers for Image Registration'
(RoTIR), a deep-learning-based method for the alignment of fish scale images
captured by light microscopy. This approach overcomes the challenge of
arbitrary rotation and translation detection, as well as the absence of ground
truth data. We employ feature-matching approaches based on Transformers and
general E(2)-equivariant steerable CNNs for model creation. Besides, an
artificial training dataset is employed for semi-supervised learning. Results
show RoTIR successfully achieves the goal of fish scale image registration.
