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

Combining high-level and low-level visual tasks is a common technique in the
field of computer vision. This work integrates the technique of image super
resolution to semantic segmentation for document image binarization. It
demonstrates that using image super-resolution as a preprocessing step can
effectively enhance the results and performance of semantic segmentation.

本文研究将图像超分辨率作为预处理步骤，与语义分割相结合，以提高文档图像二值化的结果和性能。