AADNet: Attention aware Demoiréing Network
This addresses image quality issues for photographers and users of mobile devices, but it is incremental as it builds on existing demoiréing methods with a focus on generalization.
The paper tackles the problem of removing moiré patterns from images captured by mobile devices and cameras, which degrade quality, and proposes AADNet, a lightweight network that generalizes well to unseen datasets, achieving high-fidelity results as validated on the UHDM dataset.
Moire pattern frequently appears in photographs captured with mobile devices and digital cameras, potentially degrading image quality. Despite recent advancements in computer vision, image demoire'ing remains a challenging task due to the dynamic textures and variations in colour, shape, and frequency of moire patterns. Most existing methods struggle to generalize to unseen datasets, limiting their effectiveness in removing moire patterns from real-world scenarios. In this paper, we propose a novel lightweight architecture, AADNet (Attention Aware Demoireing Network), for high-resolution image demoire'ing that effectively works across different frequency bands and generalizes well to unseen datasets. Extensive experiments conducted on the UHDM dataset validate the effectiveness of our approach, resulting in high-fidelity images.