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

RoTIR: Rotation-Equivariant Network and Transformers for Fish Scale Image Registration