Shiyang Zhou, Haijin Zeng, Congpei Qiu, Zhengcen Li, Ping Chen, Xiangming Wang, Zheng Li, Yongyong Chen · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26644 · open-access PDF
La demostración robusta de imágenes recuperadas por pantalla sigue siendo un desafío: el moiré varía en frecuencia, color y soporte espacial a través de los pares de cámaras de pantalla y condiciones de captura. Redes de restauración dedicadas entrenadas en la lucha de datos acoplados finitos con esta diversidad, especialmente cuando ocurren bandas de color de baja frecuencia y bandas de alta frecuencia. Presentamos DemoiréEdit, un marco de edición que preserva el contenido y que adapta la imagen natural antes de un editor de flujo pre-entrenado a la demostración.
Machine translation (Meta M2M100); the English original follows.
Robust demoiréing of screen-recaptured images remains challenging: moiré varies in frequency, color, and spatial support across display-camera pairs and capture conditions. Dedicated restoration networks trained on finite paired data struggle with this diversity, especially when low-frequency color banding and high-frequency stripes co-occur. We present DemoiréEdit, a content-preserving editing framework that adapts the natural-image prior of a pretrained flow-matching editor to demoiréing. First, an endpoint-supervised training objective supplements velocity matching with pixel, perceptual, a
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