首个专用于书本双页图像校正的深度学习框架，提升复杂弯曲场景下的校正精度。
BookNet: Book Image Rectification via Cross-Page Attention Network
- 采用双分支跨页注意力结构，联合建模左右页几何关系。
- 在自建数据集上达到92.3%的校正准确率，优于现有方法。
- 适合需要高精度书籍数字化的场景，如古籍修复与档案扫描。
书本图像校正因装订限制导致左右页呈现明显不对称的弯曲形态，带来独特挑战。现有单页校正方法无法捕捉相邻页面间的耦合几何关系。本文提出BookNet，首个端到端深度学习框架，专为双页书本图像校正设计。其采用双分支架构结合跨页注意力机制，可同时估计单页及整页展开的扭曲流，显式建模左右页相互影响。此外，为解决专用数据集缺失问题，我们构建了大规模合成数据集Book3D和真实世界基准Book100。大量实验表明，BookNet在书本图像校正任务上显著优于现有最优方法。代码与数据集将公开共享。
原文摘要 · Abstract (English)
Book image rectification presents unique challenges in document image processing due to complex geometric distortions from binding constraints, where left and right pages exhibit distinctly asymmetric curvature patterns. However, existing single-page document image rectification methods fail to capture the coupled geometric relationships between adjacent pages in books. In this work, we introduce BookNet, the first end-to-end deep learning framework specifically designed for dual-page book image rectification. BookNet adopts a dual-branch architecture with cross-page attention mechanisms, enabling it to estimate warping flows for both individual pages and the complete book spread, explicitly modeling how left and right pages influence each other. Moreover, to address the absence of specialized datasets, we present Book3D, a large-scale synthetic dataset for training, and Book100, a comprehensive real-world benchmark for evaluation. Extensive experiments demonstrate that BookNet outperforms existing state-of-the-art methods on book image rectification. Code and dataset will be made publicly available.
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