Document Image Binarization is a well-known problem in Document Analysis and
Computer Vision, although it is far from being solved. One of the main
challenges of this task is that documents generally exhibit degradations and
acquisition artifacts that can greatly vary throughout the page. Nonetheless,
even when dealing with a local patch of the document, taking into account the
overall appearance of a wide portion of the page can ease the prediction by
enriching it with semantic information on the ink and background conditions. In
this respect, approaches able to model both local and global information have
been proven suitable for this task. In particular, recent applications of
Vision Transformer (ViT)-based models, able to model short and long-range
dependencies via the attention mechanism, have demonstrated their superiority
over standard Convolution-based models, which instead struggle to model global
dependencies. In this work, we propose an alternative solution based on the
recently introduced Fast Fourier Convolutions, which overcomes the limitation
of standard convolutions in modeling global information while requiring fewer
parameters than ViTs. We validate the effectiveness of our approach via
extensive experimental analysis considering different types of degradations.

基于快速傅里叶卷积，提出了一种能够处理文档图像二值化问题的替代方案，克服了标准卷积在建模全局信息方面的局限性，同时比 Vision Transformer 需要更少的参数。

利用空间和频率的二值化文件处理

Binarizing Documents by Leveraging both Space and Frequency

Modern image inpainting systems, despite the significant progress, often
struggle with large missing areas, complex geometric structures, and
high-resolution images. We find that one of the main reasons for that is the
lack of an effective receptive field in both the inpainting network and the
loss function. To alleviate this issue, we propose a new method called large
mask inpainting (LaMa). LaMa is based on i) a new inpainting network
architecture that uses fast Fourier convolutions (FFCs), which have the
image-wide receptive field; ii) a high receptive field perceptual loss; iii)
large training masks, which unlocks the potential of the first two components.
Our inpainting network improves the state-of-the-art across a range of datasets
and achieves excellent performance even in challenging scenarios, e.g.
completion of periodic structures. Our model generalizes surprisingly well to
resolutions that are higher than those seen at train time, and achieves this at
lower parameter&time costs than the competitive baselines. The code is
available at https://github.com/saic-mdal/lama.

提出了一种基于快速傅里叶卷积（FFC）的图像修复网络结构的新方法 —— 大掩模修复（LaMa），该方法使用高接受场感知损失和大型训练掩模使网络具有更大的接受场，通过在各项数据集上的实验得到了优越的性能。