DSDNet: Raw Domain Demoiréing via Dual Color-Space Synergy

• URL: http://arxiv.org/abs/2504.15756v1
• Date: Tue, 22 Apr 2025 10:09:33 GMT
• Title: DSDNet: Raw Domain Demoiréing via Dual Color-Space Synergy
• Authors: Qirui Yang, Fangpu Zhang, Yeying Jin, Qihua Cheng, Pengtao Jiang, Huanjing Yue, Jingyu Yang,
• Abstract summary: We propose a single-stage raw domain demoir'eing framework, Dual-Stream Demoir'eing Network (DSDNet)<n>To guide luminance correction and moir'e removal, we design a raw-to-YCbCr mapping pipeline.<n>We also develop a Luminance-Chrominance Adaptive Transformer (LCAT) to better guide color fidelity.
• Score: 17.598942972989228
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid advancement of mobile imaging, capturing screens using smartphones has become a prevalent practice in distance learning and conference recording. However, moir\'e artifacts, caused by frequency aliasing between display screens and camera sensors, are further amplified by the image signal processing pipeline, leading to severe visual degradation. Existing sRGB domain demoir\'eing methods struggle with irreversible information loss, while recent two-stage raw domain approaches suffer from information bottlenecks and inference inefficiency. To address these limitations, we propose a single-stage raw domain demoir\'eing framework, Dual-Stream Demoir\'eing Network (DSDNet), which leverages the synergy of raw and YCbCr images to remove moir\'e while preserving luminance and color fidelity. Specifically, to guide luminance correction and moir\'e removal, we design a raw-to-YCbCr mapping pipeline and introduce the Synergic Attention with Dynamic Modulation (SADM) module. This module enriches the raw-to-sRGB conversion with cross-domain contextual features. Furthermore, to better guide color fidelity, we develop a Luminance-Chrominance Adaptive Transformer (LCAT), which decouples luminance and chrominance representations. Extensive experiments demonstrate that DSDNet outperforms state-of-the-art methods in both visual quality and quantitative evaluation, and achieves an inference speed $\mathrm{\textbf{2.4x}}$ faster than the second-best method, highlighting its practical advantages. We provide an anonymous online demo at https://xxxxxxxxdsdnet.github.io/DSDNet/.

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