  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.

Computer Vision and Pattern Recognition