Local and Global Aware Document Image Enhancement with Residual Denoising Diffusion Model

In document image enhancement scenarios, due to the limitations of high computational complexity caused by high-resolution input images, current methods often process these original degraded images by cropping them into patches of specified sizes. However, previous approaches that solely rely on cropped patches or merely use the document enhancement result of the global image as a reference are difficult to fully utilize global image information. This limitation often results in inconsistent enhancement effects across different regions of the same image. In this paper, we introduce LGA-Doc, a novel two-stage local-global information aware generative framework for document image enhancement. Our approach employs a context-aware image feature fusion module that facilitates feature interaction between local document patches and the global image, enabling deep integration of multi-granularity information. The experimental results demonstrate that our method achieves state-of-the-art performance on both the deblurring dataset and the binarization evaluation dataset. Ablation studies further validate the effectiveness of our local-global information aware module.