Pathological image super-resolution using mix-attention generative adversarial network

Image super-resolution (SR) is a fundamental research task in low-level vision. Recently it has been applied to digital pathology to build transformations from low-resolution (LR) to super-resolved high-resolution (HR) images, which benefits pathological image sharing, storage, management, etc. However, existing studies on pathological image SR are mostly carried out on simulated dataset. It cannot fully reveal the challenge of real-world SR. Meanwhile, these studies rarely investigate SR models from a pathological-tailored perspective. This paper aims to promote studies on pathological image SR from the two aspects. Firstly, we construct PathImgSR, a dataset containing real-captured paired LR-HR pathological images by leveraging the progressively imaging property of pathological images. Second, we develop MASRGAN, a GAN-based mix-attention network to implement the SR. It devises a mix-attention block that is featured by modeling the channel and spatial attentions in parallel. Therefore it better captures the discriminative feature from pathological images spatially and channel-wisely. Furthermore, by formulating the learning processing in an adversarial learning manner, it also improves the subjective perception quality of the reconstructed HR image. Experiments on PathImgSR demonstrate that MASRGAN outperforms popular CNN-based and GAN-based SR methods in both quantitative metrics and visual subjective perception.