  Due to the difficulty in collecting large-scale and perfectly aligned paired
training data for Under-Display Camera (UDC) image restoration, previous
methods resort to monitor-based image systems or simulation-based methods,
sacrificing the realness of the data and introducing domain gaps. In this work,
we revisit the classic stereo setup for training data collection -- capturing
two images of the same scene with one UDC and one standard camera. The key idea
is to "copy" details from a high-quality reference image and "paste" them on
the UDC image. While being able to generate real training pairs, this setting
is susceptible to spatial misalignment due to perspective and depth of field
changes. The problem is further compounded by the large domain discrepancy
between the UDC and normal images, which is unique to UDC restoration. In this
paper, we mitigate the non-trivial domain discrepancy and spatial misalignment
through a novel Transformer-based framework that generates well-aligned yet
high-quality target data for the corresponding UDC input. This is made possible
through two carefully designed components, namely, the Domain Alignment Module
(DAM) and Geometric Alignment Module (GAM), which encourage robust and accurate
discovery of correspondence between the UDC and normal views. Extensive
experiments show that high-quality and well-aligned pseudo UDC training pairs
are beneficial for training a robust restoration network. Code and the dataset
are available at https://github.com/jnjaby/AlignFormer.
