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

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

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 demoiréing, 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 moiré pattern removal for ultra-high-definition images. To this end, we propose the first ultra-high-definition demoiréing 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 moiré images, wherein we build a semantic-aligned scale-aware module to address the scale variation of moiré 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.

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Acknowledgements

This work is partially supported by HKU-TCL Joint Research Center for Artificial Intelligence, Hong Kong Research Grant Council - Early Career Scheme (Grant No. 27209621), National Key R &D Program of China (No.2021YFA1001300), and Guangdong-Hong Kong-Macau Applied Math Center grant 2020B1515310011.

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Correspondence to Xiaojuan Qi .

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Yu, X. et al. (2022). Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoiréing. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13678. Springer, Cham. https://doi.org/10.1007/978-3-031-19797-0_37

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