For capturing colored document images, e.g. posters and magazines, it is common that multiple degradations such as shadows, wrinkles, etc., are simultaneously introduced due to external factors. Restoring multi-degraded colored document images is a great challenge, yet overlooked, as most existing algorithms focus on enhancing color-ignored document images via binarization. Thus, we propose DocStormer, a novel algorithm designed to restore multi-degraded colored documents to their potential pristine PDF. The contributions are: firstly, we propose a "Perceive-then-Restore" paradigm with a reinforced transformer block, which more effectively encodes and utilizes the distribution of degradations. Secondly, we are the first to utilize GAN and pristine PDF magazine images to narrow the distribution gap between the enhanced results and PDF images, in pursuit of less degradation and better visual quality. Thirdly, we propose a non-parametric strategy, PFILI, which enables a smaller training scale and larger testing resolutions with acceptable detail trade-off, while saving memory and inference time. Fourthly, we are the first to propose a novel Multi-Degraded Colored Document image Enhancing dataset, named MD-CDE, for both training and evaluation. Experimental results show that the DocStormer exhibits superior performance, capable of revitalizing multi-degraded colored documents into their potential pristine digital versions, which fills the current academic gap from the perspective of method, data, and task.

参考文献提出了一种名为DocStormer的新算法，用于恢复多种退化的彩色文档图像，并使其成为潜在的原始PDF文件。该算法通过“Perceive-then-Restore”范式、GAN和纯净的PDF杂志图像等技术手段来改进彩色文档图像的质量，并提供了非参数策略PFILI和多退化彩色文档图像增强数据集（MD-CDE）。实验结果表明，DocStormer在方法、数据和任务层面上填补了当前学术研究的空白，能够将多退化的彩色文档图像恢复为潜在的原始数字版本。

DocStormer：将多种损坏的彩色文档图像恢复为原始PDF