MoiréNet: A Compact Dual-Domain Network for Image Demoiréing
This work addresses image demoiréing for applications like smartphone photography and industrial imaging, representing an incremental improvement with novel components for efficiency.
The paper tackled the problem of removing moiré patterns from digital images, which are challenging anisotropic, multi-scale artifacts, and achieved state-of-the-art performance with a 48% reduction in parameters compared to a leading method.
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.