# A Review of Document Image Enhancement Based on Document Degradation Problem
## 3 Document Image Degradation Methods
### Deblur Problem  
The process of acquiring document images (e.g., scanned images of archival contracts) in the batch may be subject to various disturbances, the most common of which are motion-type blurs such as camera shake and motion offset. Document image blurring not only makes the image content visually illegible but also leads to poor image text detection and recognition, so in-depth research on document image deblurring is needed. In 2015, [PERSON] et al. proposed using convolutional neural networks to recover blurred text documents [55]. The trained network can reconstruct high-quality images directly from blurred inputs without assuming any specific blur and noise model. The performance of convolutional networks was improved on a large number of text documents and a combination of realistic bokel and camera-dithering blur kernels. This method gives good results even if the noise level is higher than the trained model. However, the model still has room for improvement. In 2016, [PERSON] et al. proposed a simple and effective blind image deblurring method based on the dark channel prior [56]. Based on the observation of the blurring process, the L0 regularization term is used to minimize the dark channel. In the recovery process, it is in favor of the clear image rather than the blurred image to improve the model's deblurring performance. The model also has good results in non-uniform deblurring, but the accuracy of the processing effect on text images needs to be improved. In 2017, a model for cyclic consistency was proposed in Ref. [57]. It is a method for learning to transform images from a source domain X to a target domain Y without paired examples and is able to capture the relationship between high-level appearance structures. However, it has drawbacks; e.g., the accuracy is not high enough. In the same year, [PERSON] et al. applied deep multiscale convolutional neural networks to the problem of deblurring [58]. This multiscale strategy decomposes the complex problem and recovers it step by step, first recovering large-scale information at low resolution and then recovering detailed information at high resolution. The problem is simplified while increasing the receptive field of the image as a way to improve the model's ability to obtain context. However, the deblurring effect on real blurred images needs to be improved. In 2018, DeblurGAN is a GAN-based method for blind motion blur removal proposed by [PERSON] et al. [59], who were inspired by SRGAN [60] and CGAN [51] and regarded image deblurring as a  
Figure 12: Structure of the model proposed in [54], constructed by [PERSON] et al. In the training stage, the watermark w is expanded, reshaped, and concatenated with the cover image \(\mathbf{I_{c}}\). Then, a combination of the cover image \(\mathbf{I_{c}}\) and the watermark w is encoded to generate the watermarked image \(\mathbf{I_{w}}\). Next, \(\mathbf{I_{w}}\) is input into the noise layer which simulates the attacks on input, generating the noised and watermarked image \(\mathbf{I^{\prime}_{w}}\). Finally, \(\mathbf{I^{\prime}_{w}}\) is input into Decoder D to extract the watermark \(\mathbf{w}^{\prime}\) which could be similar to or the same as w. After training, Encoder E is used to embed the watermark and Decoder D is used for watermark extraction. Reproduced with permission from [PERSON] et al., _Multimedia Tools and Applications_; published by Springer Nature, 2023.  
special class of image2 image task, which is based on WGAN [61] and content loss to train the model with good performance in terms of processing speed and results. The model is mainly for the dataset with high-speed motion, and the effect for ordinary fuzzy data needs to be improved. In 2019, [PERSON] et al. came out with a blind text image deblurring algorithm using a text-specific hybrid dictionary [62]. It is a text image prior based on sparse representation, which models the relationship between an intermediate latent image and a desired sharp image and can obtain better contextual information.