  By hiding the front-facing camera below the display panel, Under-Display
Camera (UDC) provides users with a full-screen experience. However, due to the
characteristics of the display, images taken by UDC suffer from significant
quality degradation. Methods have been proposed to tackle UDC image restoration
and advances have been achieved. There are still no specialized methods and
datasets for restoring UDC face images, which may be the most common problem in
the UDC scene. To this end, considering color filtering, brightness
attenuation, and diffraction in the imaging process of UDC, we propose a
two-stage network UDC Degradation Model Network named UDC-DMNet to synthesize
UDC images by modeling the processes of UDC imaging. Then we use UDC-DMNet and
high-quality face images from FFHQ and CelebA-Test to create UDC face training
datasets FFHQ-P/T and testing datasets CelebA-Test-P/T for UDC face
restoration. We propose a novel dictionary-guided transformer network named
DGFormer. Introducing the facial component dictionary and the characteristics
of the UDC image in the restoration makes DGFormer capable of addressing blind
face restoration in UDC scenarios. Experiments show that our DGFormer and
UDC-DMNet achieve state-of-the-art performance.
