Towards Arbitrary-Scale Histopathology Image Super-resolution: An
  Efficient Dual-branch Framework via Implicit Self-texture Enhancement
Minghong Duan Linhao Qu Zhiwei Yang Manning Wang Chenxi Zhang Zhijian Song
High-quality whole-slide scanners are expensive, complex, and time-consuming,
thus limiting the acquisition and utilization of high-resolution pathology
whole-slide images in daily clinical work. Deep learning-based single-image
super-resolution techniques are an effective way to solve this problem by
synthesizing high-resolution images from low-resolution ones. However, the
existing super-resolution models applied in pathology images can only work in
fixed integer magnifications, significantly decreasing their applicability.
Though methods based on implicit neural representation have shown promising
results in arbitrary-scale super-resolution of natural images, applying them
directly to pathology images is inadequate because they have unique
fine-grained image textures different from natural images. Thus, we propose an
Implicit Self-Texture Enhancement-based dual-branch framework (ISTE) for
arbitrary-scale super-resolution of pathology images to address this challenge.
ISTE contains a pixel learning branch and a texture learning branch, which
first learn pixel features and texture features, respectively. Then, we design
a two-stage texture enhancement strategy to fuse the features from the two
branches to obtain the super-resolution results, where the first stage is
feature-based texture enhancement, and the second stage is spatial-domain-based
texture enhancement. Extensive experiments on three public datasets show that
ISTE outperforms existing fixed-scale and arbitrary-scale algorithms at
multiple magnifications and helps to improve downstream task performance. To
the best of our knowledge, this is the first work to achieve arbitrary-scale
super-resolution in pathology images. Codes will be available.