Shadows often occur when we capture the documents with casual equipment,
which influences the visual quality and readability of the digital copies.
Different from the algorithms for natural shadow removal, the algorithms in
document shadow removal need to preserve the details of fonts and figures in
high-resolution input. Previous works ignore this problem and remove the
shadows via approximate attention and small datasets, which might not work in
real-world situations. We handle high-resolution document shadow removal
directly via a larger-scale real-world dataset and a carefully designed
frequency-aware network. As for the dataset, we acquire over 7k couples of
high-resolution (2462 x 3699) images of real-world document pairs with various
samples under different lighting circumstances, which is 10 times larger than
existing datasets. As for the design of the network, we decouple the
high-resolution images in the frequency domain, where the low-frequency details
and high-frequency boundaries can be effectively learned via the carefully
designed network structure. Powered by our network and dataset, the proposed
method clearly shows a better performance than previous methods in terms of
visual quality and numerical results. The code, models, and dataset are
available at: this https URL

通过使用大规模真实世界数据集和设计精心的频率感知网络，我们直接处理高分辨率文件阴影去除，并呈现出比以前方法更好的视觉效果和数值结果。