  Moire pattern frequently appears in photographs captured with mobile devices
and digital cameras, potentially degrading image quality. Despite recent
advancements in computer vision, image demoire'ing remains a challenging task
due to the dynamic textures and variations in colour, shape, and frequency of
moire patterns. Most existing methods struggle to generalize to unseen
datasets, limiting their effectiveness in removing moire patterns from
real-world scenarios. In this paper, we propose a novel lightweight
architecture, AADNet (Attention Aware Demoireing Network), for high-resolution
image demoire'ing that effectively works across different frequency bands and
generalizes well to unseen datasets. Extensive experiments conducted on the
UHDM dataset validate the effectiveness of our approach, resulting in
high-fidelity images.
