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Dual-Scale Condition-Shift Editing for Robust Real-World Image Demoiréing

Shiyang Zhou, Haijin Zeng, Congpei Qiu, Zhengcen Li, Ping Chen, Xiangming Wang, Zheng Li, Yongyong Chen, Jingyong Su · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26644 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

スクリーンキャプチャ画像の強力なリセットアップは依然として挑戦的である: moiréは、ディスプレイカメラのカップルおよびキャプチャ条件をカバーするための周波数、色、空間サポートで異なります。この多様性と対処するために訓練された専用リセットアップネットワークは、特に低周波数色のバンディングと高周波数のストリップが同時に発生する場合に挑戦します。我々は、ディスプレイカメラのカップルおよびキャプチャ条件をカバーするためのコンテンツ保存編集フレームワークであるDemoiréEditを紹介し、プレトレーニングされたフローマッチングエディターの前に自然画像を評価する。第一に、エンドポイント監督のトレーニングオブジェクトは、ピク

Machine translation (Meta M2M100); the English original follows.

Abstract

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, and frequency-domain restoration losses. Second, a dual-scale condition-shift strategy performs global correction at low resolution, then refines the image at full resolution by shifting the denoising condition from the first-stage output to the original input for detail recovery. We also introduce DailyMoiré130, a benchmark of challenging moiré in everyday screen photography, comprising 130 smartphone captures across diverse displays with low-frequency color banding, fine high-frequency stripes, and mixed-frequency interference. On the challenging DailyMoiré130 benchmark, our method achieves the lowest residual moiré area and receives 71.3% overall-quality preference in a blind Best-of-7 study with 31 participants. We also evaluate on paired FHDMi/UHDM, reducing residual moiré confidence by 47.1%/9.3% and increasing MUSIQ by 4.3%/2.0% over the best baseline for each metric.

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Topics: Artificial intelligence, Computer science, Residual, Computer vision, Benchmark (surveying), Image editing

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