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With the rapid development of mobile devices, modern widely-used mobilephones typically allow users to capture 4K resolution (i.e.,ultra-high-definition) images. However, for image demoireing, a challengingtask in low-level vision, existing works are generally carried out onlow-resolution or synthetic images. Hence, the effectiveness of these methodson 4K resolution images is still unknown. In this paper, we explore moirepattern removal for ultra-high-definition images. To this end, we propose thefirst ultra-high-definition demoireing dataset (UHDM), which contains 5,000real-world 4K resolution image pairs, and conduct a benchmark study on currentstate-of-the-art methods. Further, we present an efficient baseline modelESDNet for tackling 4K moire images, wherein we build a semantic-alignedscale-aware module to address the scale variation of moire patterns. Extensiveexperiments manifest the effectiveness of our approach, which outperformsstate-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.

Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoireing