# A Review of Document Image Enhancement Based on Document Degradation Problem
## 3 Document Image Degradation Methods
### Page Smudging Problem  
Problems include ink penetration, prominent smudges, and yellowing of paper in the original document due to improper preservation. We attribute this type of problems to page smudging in degradation tasks. In addition to the traditional binarization method, the following models have better effects on this kind of problem. In 2018, a hierarchical deep supervised network (DSN) architecture [28] was proposed by [PERSON] et al. It applied the deep supervised network model to the document image binarization issues for the first time, which could better distinguish the foreground from background noise to predict text pixels with different feature levels. However, the large number of convolutional layers leads to the problem of processing small amounts of data as well as long processing times. In the same year, [PERSON] et al. proposed a game theory-inspired binarization technique [29], which designs a two-player, non-zero-sum algorithm at the pixel level to extract local information from document images, and then feeds the  
Figure 3: The structure of FD-Net, constructed by [PERSON] et al. [27]. Reproduced with permission from [PERSON] et al., Springer eBook; published by Springer Nature, 2021.  
obtained results into the K-means algorithm for pixel classification. The pre-processing step and the post-processing step of the method also contribute to the enhancement of the overall algorithm, eliminating the intensity variations that often occur in the background and refining the results. In 2019, [PERSON] et al. proposed a selective auto-coding approach [30] that performs a transformation step on the input signal using a convolutional auto-encoder and adjusts these transformation parameters during the training process to achieve model optimization, and finally applies a global threshold to binarize the document, as shown in Figure 4. For different input image sizes and when the convolutional network uses a different number of convolutional kernels, the model can have good binarization results, and the convolutional kernel size does not have a large impact on the binarization results. However, the number of model parameters needs to be increased due to the use of step size instead of the pooling layer in the downsampling process. In the same year, [PERSON] et al. introduced conditional generative adversarial networks (cGAN) to solve the core problem of multi-scale information combinations in binarization tasks [31], as shown in Figure 5. The model has a good effect on degradation problems such as page smudging, but the drawback is that the training strategy of the sub-generator still needs to be modified, and the update efficiency and scalability of the model still need to be improved. [PERSON] et al. used a multi-resolution attention model to infer the probability of text areas and then feed it into a convolutional random field (ConvCRF) to obtain the final result [32]. This method uses distance reciprocal distortion measure loss to infer the relationship between text regions and background by ConvCRF, which has a stronger generalization ability. However, the training and inference speed of ConvCRF is slow compared to other models, and its parameters are difficult to learn. In 2020, [PERSON] et al. proposed the model of DE-GAN [33], which transforms the threshold setting task of processing text and background into an image-to-image  
Figure 4: Model structure constructed by [PERSON] and [PERSON] [30]. The output layer consists of the activation level assigned to each input feature (white denotes maximum activation). Published under a Creative Commons Attribution 4.0 license. Figure 5: The structure of the model constructed by [PERSON] et al. [31]. The whole-image generator is composed of G1 and G2. The two sub-generators are cascaded, and G2 optimizes the results of G1. Reproduced with permission from [PERSON] et al., _Pattern Recognition_; published by Elsevier, 2019.  
transformation task.