  Under-display camera (UDC) provides an elegant solution for full-screen
smartphones. However, UDC captured images suffer from severe degradation since
sensors lie under the display. Although this issue can be tackled by image
restoration networks, these networks require large-scale image pairs for
training. To this end, we propose a modular network dubbed MPGNet trained using
the generative adversarial network (GAN) framework for simulating UDC imaging.
Specifically, we note that the UDC imaging degradation process contains
brightness attenuation, blurring, and noise corruption. Thus we model each
degradation with a characteristic-related modular network, and all modular
networks are cascaded to form the generator. Together with a pixel-wise
discriminator and supervised loss, we can train the generator to simulate the
UDC imaging degradation process. Furthermore, we present a Transformer-style
network named DWFormer for UDC image restoration. For practical purposes, we
use depth-wise convolution instead of the multi-head self-attention to
aggregate local spatial information. Moreover, we propose a novel channel
attention module to aggregate global information, which is critical for
brightness recovery. We conduct evaluations on the UDC benchmark, and our
method surpasses the previous state-of-the-art models by 1.23 dB on the P-OLED
track and 0.71 dB on the T-OLED track, respectively.
