# A Review of Document Image Enhancement Based on Document Degradation Problem
## 3 Document Image Degradation Methods
### Watermark Removal Problem  
Watermarking is the most common way to protect the copyright of an image. Many organizations will add watermarks to their product images to prevent them from being stolen by others. We must recover the real image from the watermarked image if the original image is lost. Watermark removal can be divided into two main methods: the first is a single-stage target detection method, and the second is a two-stage target detection method. Next, typical models of the two methods are listed. The single-stage target detection method is as follows. In 2019, [PERSON] et al. designed and tested an encoder-decoder model with an eight-level jump connection [47] to improve the quality of scanned documents with better results for removing watermarks from documents, but experimental and quantitative evaluations were inadequate. In 2020, DE-GAN was proposed [33], a more flexible model that is useful for watermark removal and other document degradation problems such as deblurring. However, it is computationally complex and requires predetermined parameters to be adjusted for different images. In 2022, [PERSON]. [48] proposed an end-to-end architecture based on GANs with the addition of a handwritten text recognizer, where text information patches exist in handwritten text recognition (HTR) form during training to increase the readability of the text and facilitate document watermark removal, as shown in Figure 10. However, the use of the results for unpaired data is not yet as advantageous. The two-stage target detection method is as follows. In 2019, [PERSON] et al. proposed a novel two-stage separable deep learning (TSDL) framework [49] consisting of noise-free end-to-end adversary training (FEAT) and noise-aware decoder-only training (ADOT). A redundant multilayer feature encoding network (RMR-FEN) was designed as an encoder framework that can learn a robust watermarking pattern without seeing any noise, can resist high-intensity noise, and has better stability, greater performance, and faster convergence speed. However, it is necessary to set the parameters manually, resulting in the need to manually set the results of the algorithm according to the features of different images, which leads to a decrease in model efficiency when facing multiple datasets. In 2020, [PERSON] et al. proposed a visible watermark removal network architecture [50] based on conditional generative adversarial networks (CGAN) [51] and least squares generative adversarial networks (LSGAN) [52]. As shown in Figure 11, the  
Figure 9: CANet model structure constructed by [PERSON] et al. [45]. At Stage I, the contextual feature is firstly extracted via a pre-trained DenseNet; meanwhile, the designed contextual patch matching module (CPM) is used to acquire a set of contextual matching pairs; then, a contextual feature transfer mechanism is applied to transfer contextual information from non-shadow patches to shadow patches to recover the L and A/B channels of the shadow-removal image. At Stage II, the recovered L and A/B channel information is integrated with the input shadow image and fed into a DenseUNet to generate the final shadow-removal result. Published under a Creative Commons Attribution 4.0 license. proposed model can automatically detect the watermark location without manual checking, which improves the model efficiency, but the generalization ability of the model is weak. In 2021, WDNet was proposed by [PERSON] et al. [53], which is a network dedicated to watermark removal. It does not remove the watermark but separates the watermark and uses this watermark to build a larger dataset, which can better segment the real image and the watermark with higher accuracy. However, the segmentation accuracy for color images still needs to be improved. In 2023, [PERSON] et al. proposed an end-to-end document image watermarking scheme using deep neural networks [54], as shown in Figure 12. A noise layer is added to simulate various attacks that may be encountered in reality, such as cropout, dropout, Gaussian blur, etc.