轻量级网络有效去除图像摩尔纹，兼顾质量与效率
MoiréNet: A Compact Dual-Domain Network for Image Demoiréing
- 结合频域与空域特征，用方向差分卷积识别摩尔纹方向
- 仅551.3万参数，比ESDNet-L少48%，仍达顶尖效果
- 适合手机摄影、工业成像等资源受限场景
摩尔纹源于显示像素阵列与相机传感器网格之间的频谱混叠，表现为各向异性的多尺度伪影，给数字图像去摩尔纹带来巨大挑战。本文提出MoiréNet，一种基于卷积神经网络的U-Net框架，协同融合频率域与空间域特征以实现有效伪影消除。MoiréNet引入两个关键组件：方向频域-空域编码器（DFSE），通过方向差分卷积辨识摩尔纹取向；频域-空域自适应选择器（FSAS），实现精准且特征自适应的抑制。大量实验表明，MoiréNet在公开及实际使用数据集上均达到当前最优性能，同时具备极高的参数效率。仅含551.3万参数，较ESDNet-L减少48%，在保持优异重建质量的同时，特别适用于智能手机摄影、工业成像及增强现实等资源受限应用。
原文摘要 · Abstract (English)
Moiré 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 demoiréing. We propose MoiréNet, a convolutional neural U-Net-based framework that synergistically integrates frequency and spatial domain features for effective artifact removal. MoiréNet introduces two key components: a Directional Frequency-Spatial Encoder (DFSE) that discerns moiré orientation via directional difference convolution, and a Frequency-Spatial Adaptive Selector (FSAS) that enables precise, feature-adaptive suppression. Extensive experiments demonstrate that MoiréNet 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, MoiréNet 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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