With the growing importance of preventing the COVID-19 virus, face images obtained in most video surveillance scenarios are low resolution with mask simultaneously. However, most of the previous face super-resolution solutions can not handle both tasks in one model. In this work, we treat the mask occlusion as image noise and construct a joint and collaborative learning network, called JDSR-GAN, for the masked face super-resolution task. Given a low-quality face image with the mask as input, the role of the generator composed of a denoising module and super-resolution module is to acquire a high-quality high-resolution face image. The discriminator utilizes some carefully designed loss functions to ensure the quality of the recovered face images. Moreover, we incorporate the identity information and attention mechanism into our network for feasible correlated feature expression and informative feature learning. By jointly performing denoising and face super-resolution, the two tasks can complement each other and attain promising performance. Extensive qualitative and quantitative results show the superiority of our proposed JDSR-GAN over some comparable methods which perform the previous two tasks separately.

翻译：随着预防COVID-19病毒的重要性日益增加，大多数视频监控场景中获取的人脸图像同时存在低分辨率和口罩遮挡问题。然而，以往大多数人脸超分辨率解决方案无法在单一模型中同时处理这两个任务。在本工作中，我们提出将口罩遮挡视为图像噪声，并构建一个名为JDSR-GAN的联合协同学习网络，用于口罩遮挡人脸超分辨率任务。给定一张带有口罩的低质量人脸图像作为输入，由去噪模块和超分辨率模块组成的生成器用于获得高质量高分辨率人脸图像。判别器通过采用精心设计的损失函数确保恢复人脸图像的质量。此外，我们将身份信息与注意力机制引入网络，以实现可行的相关特征表达和信息特征学习。通过联合执行去噪和人脸超分辨率任务，两者能够相互补充并取得优异性能。大量定性和定量结果表明，我们提出的JDSR-GAN相较于分别执行上述两个任务的对比方法具有显著优越性。