MoiréNet: A Compact Dual-Domain Network for Image Demoiréing

• URL: http://arxiv.org/abs/2509.18910v1
• Date: Tue, 23 Sep 2025 12:33:23 GMT
• Title: MoiréNet: A Compact Dual-Domain Network for Image Demoiréing
• Abstract summary: Moir'e patterns arise from spectral aliasing between display pixel lattices and camera sensor grids.<n>We propose Moir'eNet, a convolutional neural U-Net-based framework that integrates frequency and spatial domain features for effective artifact removal.
• Score: 14.959312694378765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Moir\'e patterns arise from spectral aliasing between display pixel lattices and camera sensor grids, manifesting as anisotropic, multi-scale artifacts that pose significant challenges for digital image demoir\'eing. We propose Moir\'eNet, a convolutional neural U-Net-based framework that synergistically integrates frequency and spatial domain features for effective artifact removal. Moir\'eNet introduces two key components: a Directional Frequency-Spatial Encoder (DFSE) that discerns moir\'e orientation via directional difference convolution, and a Frequency-Spatial Adaptive Selector (FSAS) that enables precise, feature-adaptive suppression. Extensive experiments demonstrate that Moir\'eNet achieves state-of-the-art performance on public and actively used datasets while being highly parameter-efficient. With only 5.513M parameters, representing a 48% reduction compared to ESDNet-L, Moir\'eNet combines superior restoration quality with parameter efficiency, making it well-suited for resource-constrained applications including smartphone photography, industrial imaging, and augmented reality.

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