  Image restoration networks are usually comprised of an encoder and a decoder,
responsible for aggregating image content from noisy, distorted data and to
restore clean, undistorted images, respectively. Data aggregation as well as
high-resolution image generation both usually come at the risk of involving
aliases, i.e.~standard architectures put their ability to reconstruct the model
input in jeopardy to reach high PSNR values on validation data. The price to be
paid is low model robustness. In this work, we show that simply providing
alias-free paths in state-of-the-art reconstruction transformers supports
improved model robustness at low costs on the restoration performance. We do so
by proposing BOA-Restormer, a transformer-based image restoration model that
executes downsampling and upsampling operations partly in the frequency domain
to ensure alias-free paths along the entire model while potentially preserving
all relevant high-frequency information.
