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.

透过针对不同频带的动态材质和动态纹理的莫尔纹理的变化，提出了一种新型轻量级构架，称为 AADNet（注意力感知莫尔纹去除网络），用于高分辨率图像的莫尔纹去除。广泛的实验证实了我们的方法的有效性，并产生了高保真的图像。

AADNet：注意力感知去莫尔网

AADNet: Attention aware Demoiréing Network

Interference between overlapping gird patterns creates moire patterns,
degrading the visual quality of an image that captures a screen of a digital
display device by an ordinary digital camera. Removing such moire patterns is
challenging due to their complex patterns of diverse sizes and color
distortions. Existing approaches mainly focus on filtering out in the spatial
domain, failing to remove a large-scale moire pattern. In this paper, we
propose a novel model called FPANet that learns filters in both frequency and
spatial domains, improving the restoration quality by removing various sizes of
moire patterns. To further enhance, our model takes multiple consecutive
frames, learning to extract frame-invariant content features and outputting
better quality temporally consistent images. We demonstrate the effectiveness
of our proposed method with a publicly available large-scale dataset, observing
that ours outperforms the state-of-the-art approaches, including ESDNet,
VDmoire, MBCNN, WDNet, UNet, and DMCNN, in terms of the image and video quality
metrics, such as PSNR, SSIM, LPIPS, FVD, and FSIM.

本文提出了一种名为 FPANet 的模型，该模型在频率和空间域中学习滤波器，通过学习提取帧不变特征和输出更好质量的时间一致图像，改善了移除各种大小 Moire 图案的恢复质量，并用公开数据集展示了其优越性。