  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.
