ENet-GP：统一文档图像恢复

中文摘要 AI 辅助

在不受控制的拍摄环境下进行可靠的文档数字化具有挑战性，因为真实图像表现出多种相互作用的退化，而非单一的孤立失真。由此拍摄的文档同时受到几何失真（如页面弯曲）以及光度退化（如不均匀照明和模糊）的影响。然而，大多数现有方法独立处理这些因素，并在仅包含一种失真类型的基准上进行评估，限制了其在实际应用中的适用性。我们引入了GutenDoc，一个包含高分辨率密集文本文档的大规模数据集，其具有基于物理的复合退化。利用基于物理的渲染，我们的数据集联合建模了几何弯曲和多种光度效应，从而能够在真实拍摄条件下进行系统评估。我们进一步提出了一个统一的恢复框架，在单网络和单训练设置中联合校正几何和光度失真，无需针对特定退化的重新训练或顺序推理过程。大量实验表明，我们的方法在已建立的单失真基准上保持竞争力，同时在复合退化下显著提高了鲁棒性，为实际文档数字化提供了实用解决方案。

英文摘要

Reliable document digitization in uncontrolled capture settings is challenging because real images exhibit multiple interacting degradations rather than a single isolated distortion. Documents thus captured are affected simultaneously by geometric distortions, like page warping, as well as photometric degradations such as non-uniform illumination, and blurring. However, most existing approaches address these factors independently and are evaluated on benchmarks containing only one distortion type, limiting their real-world applicability. We introduce GutenDoc, a large-scale dataset of high-resolution dense-text documents with physically grounded compound degradations. Using physics-based rendering, our dataset jointly models geometric warping and diverse photometric effects, enabling systematic evaluation under realistic capture conditions. We further propose a unified restoration framework that jointly corrects geometric and photometric distortions within a single-network and single-training setup, without the need for degradation-specific retraining or sequential inference passes. Extensive experiments show that our method remains competitive on established single-distortion benchmarks while substantially improving robustness under compound degradations, providing a practical solution for real-world document digitization.

发表机构

• Indian Institute of Technology, Madras（马德拉斯印度理工学院）
• Adobe, India（奥多比公司印度分部）

机构由 AI 辅助整理，请以论文原文为准。