论文标题
重新思考降解：X光片超级分辨率通过AID-SRGAN
Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN
论文作者
论文摘要
在本文中，我们提出了一项医疗措施，以确定超级分辨率生成对抗网络（AID-SRGAN），以实现二线图像超分辨率。首先，我们提出了一种医学实践降解模型，该模型考虑了除了减少采样以外的各种退化因素。据我们所知，这是针对射线照相图像提出的第一个复合降解模型。此外，我们提出了AID-SRGAN，它可以同时降解并产生高分辨率（HR）X光片。在此模型中，我们将注意力机制引入了denoising模块中，以使其对复杂的降解更加健壮。最后，SR模块使用“清洁”低分辨率（LR）X光片重建了HR X光片。此外，我们提出了一种单独的接头训练方法来训练模型，并进行了广泛的实验，以表明所提出的方法优于其对应物。例如，我们提出的方法可实现$ 31.90 $的PSNR，比例因子为$ 4 \ times $，比最近的工作（SPSR [16]）高7.05美元\％$ $。我们的数据集和代码将在以下网址提供：https：//github.com/yongsongh/aidsrgan-miccai2022。
In this paper, we present a medical AttentIon Denoising Super Resolution Generative Adversarial Network (AID-SRGAN) for diographic image super-resolution. First, we present a medical practical degradation model that considers various degradation factors beyond downsampling. To the best of our knowledge, this is the first composite degradation model proposed for radiographic images. Furthermore, we propose AID-SRGAN, which can simultaneously denoise and generate high-resolution (HR) radiographs. In this model, we introduce an attention mechanism into the denoising module to make it more robust to complicated degradation. Finally, the SR module reconstructs the HR radiographs using the "clean" low-resolution (LR) radiographs. In addition, we propose a separate-joint training approach to train the model, and extensive experiments are conducted to show that the proposed method is superior to its counterparts. e.g., our proposed method achieves $31.90$ of PSNR with a scale factor of $4 \times$, which is $7.05 \%$ higher than that obtained by recent work, SPSR [16]. Our dataset and code will be made available at: https://github.com/yongsongH/AIDSRGAN-MICCAI2022.
