Chest X-ray (CXR) is a medical imaging technology that is common and economical to use in clinical. Recently, coronavirus (COVID-19) has spread worldwide, and the second wave is rebounding strongly now with the coming winter that has a detrimental effect on the global economy and health. To make pre-diagnosis of COVID-19 as soon as possible, and reduce the work pressure of medical staff, making use of deep learning networks to detect positive CXR images of infected patients is a critical step. However, there are complex edge structures and rich texture details in the CXR images susceptible to noise that can interfere with the diagnosis of the machines and the doctors. Therefore, in this paper, we proposed a novel multi-resolution parallel residual CNN (named MPR-CNN) for CXR images denoising and special application for COVID-19 which can improve the image quality. The core of MPR-CNN consists of several essential modules. (a) Multi-resolution parallel convolution streams are utilized for extracting more reliable spatial and semantic information in multi-scale features. (b) Efficient channel and spatial attention can let the network focus more on texture details in CXR images with fewer parameters. (c) The adaptive multi-resolution feature fusion method based on attention is utilized to improve the expression of the network. On the whole, MPR-CNN can simultaneously retain spatial information in the shallow layers with high resolution and semantic information in the deep layers with low resolution. Comprehensive experiments demonstrate that our MPR-CNN can better retain the texture structure details in CXR images. Additionally, extensive experiments show that our MPR-CNN has a positive impact on CXR images classification and detection of COVID-19 cases from denoised CXR images.

胸部X射线（CXR）是临床上常用且经济的医学成像技术。最近，冠状病毒（COVID-19）在全球范围内传播，随着冬季即将到来，第二波疫情正在强劲反弹，对全球经济和健康产生不利影响。为了尽快对COVID-19进行预诊断，减轻医护人员的工作压力，利用深度学习网络检测感染患者的CXR阳性图像是关键的一步。然而，CXR 图像中存在复杂的边缘结构和丰富的纹理细节，容易受到噪声的影响，从而干扰机器和医生的诊断。因此，在本文中，我们提出了一种新颖的多分辨率并行残差 CNN（称为 MPR-CNN），用于 CXR 图像去噪和针对 COVID-19 的特殊应用，可以提高图像质量。 MPR-CNN 的核心由几个基本模块组成。 （a）利用多分辨率并行卷积流来提取多尺度特征中更可靠的空间和语义信息。 (b) 高效的通道和空间注意力可以让网络以更少的参数更多地关注 CXR 图像中的纹理细节。 (c)利用基于注意力的自适应多分辨率特征融合方法来提高网络的表达能力。总体而言，MPR-CNN能够同时保留浅层高分辨率的空间信息和深层低分辨率的语义信息。综合实验表明，我们的 MPR-CNN 可以更好地保留 CXR 图像中的纹理结构细节。 此外，大量实验表明，我们的 MPR-CNN 对 CXR 图像分类和从去噪 CXR 图像中检测 COVID-19 病例具有积极影响。