Background and objective

With the increasing problem of coronavirus disease 2019 (COVID-19) in the world, improving the image resolution of COVID-19 computed tomography (CT) becomes a very important task. At present, single-image super-resolution (SISR) models based on convolutional neural networks (CNN) generally have problems such as the loss of high-frequency information and the large size of the model due to the deep network structure.

Methods

In this work, we propose an optimization model based on multi-window back-projection residual network (MWSR), which outperforms most of the state-of-the-art methods. Firstly, we use multi-window to refine the same feature map at the same time to obtain richer high/low frequency information, and fuse and filter out the features needed by the deep network. Then, we develop a back-projection network based on the dilated convolution, using up-projection and down-projection modules to extract image features. Finally, we merge several repeated and continuous residual modules with global features, merge the information flow through the network, and input them to the reconstruction module.

Results

The proposed method shows the superiority over the state-of-the-art methods on the benchmark dataset, and generates clear COVID-19 CT super-resolution images.

Conclusion

Both subjective visual effects and objective evaluation indicators are improved, and the model specifications are optimized. Therefore, the MWSR method can improve the clarity of CT images of COVID-19 and effectively assist the diagnosis and quantitative assessment of COVID-19.

中文翻译：

---

用于重建 COVID-19 CT 超分辨率图像的多窗口反投影残差网络

 背景和目标

随着2019冠状病毒病（COVID-19）在全球范围内的日益严重，提高COVID-19计算机断层扫描（CT）的图像分辨率成为一项非常重要的任务。目前，基于卷积神经网络（CNN）的单图像超分辨率（SISR）模型普遍存在因深层网络结构导致高频信息丢失、模型尺寸过大等问题。

 方法

在这项工作中，我们提出了一种基于多窗口反投影残差网络（MWSR）的优化模型，该模型优于大多数最先进的方法。首先，我们使用多窗口同时细化同一特征图以获得更丰富的高/低频信息，并融合和过滤掉深层网络所需的特征。然后，我们开发了一个基于扩张卷积的反投影网络，使用上投影和下投影模块来提取图像特征。最后，我们合并几个具有全局特征的重复且连续的残差模块，合并通过网络的信息流，并将其输入到重建模块。

 结果

该方法在基准数据集上显示出优于最先进方法的优越性，并生成清晰的 COVID-19 CT 超分辨率图像。

 结论

主观视觉效果和客观评价指标均得到提升，模型规格得到优化。因此，MWSR方法可以提高COVID-19 CT图像的清晰度，有效辅助COVID-19的诊断和定量评估。