MoiréNet: Leveraging Directional Priors for Compact Dual-Domain Image Demoiréing

Digital images serve as the fundamental carrier for information exchange within multimedia ecosystems. However, the ubiquitous practice of screen recapturing often introduces complex moiré patterns due to spectral aliasing, which severely degrades the visual quality and impedes downstream multimedia analysis tasks. In this paper, we propose MoiréNet, a compact convolutional neural framework designed for effective moiré removal, thereby synergistically restoring high-fidelity content. To address the anisotropic and multi-scale nature of these artifacts, we introduce two novel modules: the Directional Frequency-Spatial Encoder (DFSE), which explicitly discerns moiré orientation via directional difference convolutions, and the Frequency-Spatial Adaptive Selector (FSAS), which enables feature-adaptive artifact suppression across dual domains. Extensive experiments demonstrate that MoiréNet achieves state-of-the-art performance on public and widely 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 for storage-constrained multimedia applications.