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.

翻译：摩尔纹图案频繁出现在用移动设备及数码相机拍摄的照片中，可能降低图像质量。尽管计算机视觉领域近期取得进展，但由于摩尔纹图案在颜色、形状和频率上呈现动态纹理与变化，图像去摩尔纹仍是一项具有挑战性的任务。现有方法大多难以泛化至未见过的数据集，限制了其在真实场景中去除摩尔纹的效果。本文提出一种新颖的轻量级架构AADNet（注意力感知去摩尔纹网络），用于高分辨率图像去摩尔纹，该网络能有效跨越不同频段工作，并具有良好的跨数据集泛化能力。在UHDM数据集上开展的广泛实验验证了我们方法的有效性，能够生成高保真图像。