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Existing super-resolution models for pathology images can only work in fixedinteger magnifications and have limited performance. Though implicit neuralnetwork-based methods have shown promising results in arbitrary-scalesuper-resolution of natural images, it is not effective to directly apply themin pathology images, because pathology images have special fine-grained imagetextures different from natural images. To address this challenge, we propose adual-branch framework with an efficient self-texture enhancement mechanism forarbitrary-scale super-resolution of pathology images. Extensive experiments ontwo public datasets show that our method outperforms both existing fixed-scaleand arbitrary-scale algorithms. To the best of our knowledge, this is the firstwork to achieve arbitrary-scale super-resolution in the field of pathologyimages. Codes will be available.

Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement