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

• URL: http://arxiv.org/abs/2304.04238v1
• Date: Sun, 9 Apr 2023 13:38:18 GMT
• Title: Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement
• Authors: Linhao Qu, Minghong Duan, Zhiwei Yang, Manning Wang, Zhijian Song
• Abstract summary: Super-resolution models for pathology images can only work in fixed integer magnifications and have limited performance. We propose a dual-branch framework with an efficient self-texture enhancement mechanism for arbitrary-scale super-resolution of pathology images.
• Score: 6.374541716921289
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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