Removing degradation from document images not only improves their visual
quality and readability, but also enhances the performance of numerous
automated document analysis and recognition tasks. However, existing
regression-based methods optimized for pixel-level distortion reduction tend to
suffer from significant loss of high-frequency information, leading to
distorted and blurred text edges. To compensate for this major deficiency, we
propose DocDiff, the first diffusion-based framework specifically designed for
diverse challenging document enhancement problems, including document
deblurring, denoising, and removal of watermarks and seals. DocDiff consists of
two modules: the Coarse Predictor (CP), which is responsible for recovering the
primary low-frequency content, and the High-Frequency Residual Refinement (HRR)
module, which adopts the diffusion models to predict the residual
(high-frequency information, including text edges), between the ground-truth
and the CP-predicted image. DocDiff is a compact and computationally efficient
model that benefits from a well-designed network architecture, an optimized
training loss objective, and a deterministic sampling process with short time
steps. Extensive experiments demonstrate that DocDiff achieves state-of-the-art
(SOTA) performance on multiple benchmark datasets, and can significantly
enhance the readability and recognizability of degraded document images.
Furthermore, our proposed HRR module in pre-trained DocDiff is plug-and-play
and ready-to-use, with only 4.17M parameters. It greatly sharpens the text
edges generated by SOTA deblurring methods without additional joint training.
Available codes: this https URL

提出了一种名为 DocDiff 的基于扩散的框架，用于解决文档图像退化问题，包括去模糊、去噪和去水印，采用回归和扩散模型结合的方法来处理文档图像的高频信息，已在多个基准数据集上取得了最先进的性能（SOTA）表现。

DocDiff: 通过剩余扩散模型增强文档

DocDiff: Document Enhancement via Residual Diffusion Models

Documents often exhibit various forms of degradation, which make it hard to
be read and substantially deteriorate the performance of an OCR system. In this
paper, we propose an effective end-to-end framework named Document Enhancement
Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs)
to restore severely degraded document images. To the best of our knowledge,
this practice has not been studied within the context of generative adversarial
deep networks. We demonstrate that, in different tasks (document clean up,
binarization, deblurring and watermark removal), DE-GAN can produce an enhanced
version of the degraded document with a high quality. In addition, our approach
provides consistent improvements compared to state-of-the-art methods over the
widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its
ability to restore a degraded document image to its ideal condition. The
obtained results on a wide variety of degradation reveal the flexibility of the
proposed model to be exploited in other document enhancement problems.

该研究提出了一种名为 DE-GAN 的文档增强生成对抗网络框架，通过条件 GAN 对严重降解的文档图像进行恢复，实验证明 DE-GAN 可以在文本清理，二值化，去除模糊和去水印等不同任务中产生高质量的文档修复版本，并且相对于其他最先进的方法，它能够提供持续的改进，证明了该模型将被应用在其他文档增强问题上的灵活性。

DE-GAN：一种用于文档增强的有条件生成对抗网络

DE-GAN: A Conditional Generative Adversarial Network for Document  Enhancement

This paper presents a novel iterative deep learning framework and apply it
for document enhancement and binarization. Unlike the traditional methods which
predict the binary label of each pixel on the input image, we train the neural
network to learn the degradations in document images and produce the uniform
images of the degraded input images, which allows the network to refine the
output iteratively. Two different iterative methods have been studied in this
paper: recurrent refinement (RR) which uses the same trained neural network in
each iteration for document enhancement and stacked refinement (SR) which uses
a stack of different neural networks for iterative output refinement. Given the
learned uniform and enhanced image, the binarization map can be easy to obtain
by a global or local threshold. The experimental results on several public
benchmark data sets show that our proposed methods provide a new clean version
of the degraded image which is suitable for visualization and promising results
of binarization using the global Otsu's threshold based on the enhanced images
learned iteratively by the neural network.

本文提出了一种新的迭代深度学习框架，并将其应用于文档增强和二值化。通过学习文档图像中的退化过程，该网络能够逐步细化输出，实现文档增强。通过在全局或本地阈值下使用增强的图像，可以获得二值化图。实验结果表明，所提出的方法适用于可视化和基于 Otsu 阈值的二值化处理。