  Existing super-resolution models for pathology images can only work in fixed
integer magnifications and have limited performance. Though implicit neural
network-based methods have shown promising results in arbitrary-scale
super-resolution of natural images, it is not effective to directly apply them
in pathology images, because pathology images have special fine-grained image
textures different from natural images. To address this challenge, we propose a
dual-branch framework with an efficient self-texture enhancement mechanism for
arbitrary-scale super-resolution of pathology images. Extensive experiments on
two public datasets show that our method outperforms both existing fixed-scale
and arbitrary-scale algorithms. To the best of our knowledge, this is the first
work to achieve arbitrary-scale super-resolution in the field of pathology
images. Codes will be available.
