  Under-Display Camera (UDC) is an emerging technology that achieves
full-screen display via hiding the camera under the display panel. However, the
current implementation of UDC causes serious degradation. The incident light
required for camera imaging undergoes attenuation and diffraction when passing
through the display panel, leading to various artifacts in UDC imaging.
Presently, the prevailing UDC image restoration methods predominantly utilize
convolutional neural network architectures, whereas Transformer-based methods
have exhibited superior performance in the majority of image restoration tasks.
This is attributed to the Transformer's capability to sample global features
for the local reconstruction of images, thereby achieving high-quality image
restoration. In this paper, we observe that when using the Vision Transformer
for UDC degraded image restoration, the global attention samples a large amount
of redundant information and noise. Furthermore, compared to the ordinary
Transformer employing dense attention, the Transformer utilizing sparse
attention can alleviate the adverse impact of redundant information and noise.
Building upon this discovery, we propose a Segmentation Guided Sparse
Transformer method (SGSFormer) for the task of restoring high-quality images
from UDC degraded images. Specifically, we utilize sparse self-attention to
filter out redundant information and noise, directing the model's attention to
focus on the features more relevant to the degraded regions in need of
reconstruction. Moreover, we integrate the instance segmentation map as prior
information to guide the sparse self-attention in filtering and focusing on the
correct regions.
