  With the rapid development of mobile devices, modern widely-used mobile
phones typically allow users to capture 4K resolution (i.e.,
ultra-high-definition) images. However, for image demoireing, a challenging
task in low-level vision, existing works are generally carried out on
low-resolution or synthetic images. Hence, the effectiveness of these methods
on 4K resolution images is still unknown. In this paper, we explore moire
pattern removal for ultra-high-definition images. To this end, we propose the
first ultra-high-definition demoireing dataset (UHDM), which contains 5,000
real-world 4K resolution image pairs, and conduct a benchmark study on current
state-of-the-art methods. Further, we present an efficient baseline model
ESDNet for tackling 4K moire images, wherein we build a semantic-aligned
scale-aware module to address the scale variation of moire patterns. Extensive
experiments manifest the effectiveness of our approach, which outperforms
state-of-the-art methods by a large margin while being much more lightweight.
Code and dataset are available at https://xinyu-andy.github.io/uhdm-page.
