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ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2026,6,15]]},"abstract":"<jats:p>The widespread use of mobile devices and electronic displays has made camera-to-screen interaction, where users capture display screens with smartphones or smart glasses, a common way of recording and sharing information across applications such as real-time document analysis and AR\/VR. However, such interaction often introduces unwanted colored stripe distortions known as moir\u00e9 patterns. These patterns severely degrade image quality and hinder user experience in downstream tasks. Existing moir\u00e9 removal methods process entire images uniformly, leading to high computational costs and detail degradation in moir\u00e9-free regions, while lacking generalizability across diverse real-world scenarios. In this paper, we propose Moir\u00e9Mop, a lightweight, high-fidelity, and pervasive moir\u00e9 removal method for mobile devices. Moir\u00e9Mop localizes moir\u00e9-affected regions robustly and performs selective removal for efficiency and fidelity. To achieve robust and fine-grained moir\u00e9 region extraction, we analyze the spectral aliasing mechanism of moir\u00e9 pattern and reveal a unique symmetry between high- and low-frequency components. Leveraging this, we propose a Reversed Spectrum Consistency Map (RSCM) to estimate moir\u00e9 presence and a Temporal Consistency Mask Propagation (TCMP) for real-time moir\u00e9 region propagation across frames. For high-fidelity moir\u00e9 removal, a Cross Spectral Adaptive Demoir\u00e9 Network (CSADN) is designed to suppress moir\u00e9 adaptively by modeling frequency symmetry. Extensive experiments demonstrate that Moir\u00e9Mop achieves high-fidelity moir\u00e9 removal and enhances segmentation accuracy by 18% while maintaining real-time efficiency on pervasive mobile devices, significantly improving user experience.<\/jats:p>","DOI":"10.1145\/3810197","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T17:06:41Z","timestamp":1781543201000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Moir\u00e9Mop: Lightweight and Pervasive Moir\u00e9 Removal for Mobile Camera-to-Screen Interaction"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6042-5032","authenticated-orcid":false,"given":"Zhaowei","family":"Wu","sequence":"first","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5075-8512","authenticated-orcid":false,"given":"Jingyi","family":"Ning","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8207-1125","authenticated-orcid":false,"given":"Yanling","family":"Bu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2994-6743","authenticated-orcid":false,"given":"Lei","family":"Xie","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2025. 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