# A Review of Document Image Enhancement Based on Document Degradation Problem
## 3 Document Image Degradation Methods
### Experiment  
We conducted experiments based on 10 DIBCO and H-DIBCO test datasets for the background texture and page smudge problems. The handwriting fading problem was investigated with experiments for the DIBCO2018 dataset. The results are shown in Table 2. The experimental results of the illumination non-uniformity problem are shown in Table 3. The experimental results of the deblurring problem are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Degradation** & **Dataset** & **Method** & **PSNR** & **SSIM** & **CER** \\ \hline \multirow{6}{*}{deblurring} & & [PERSON] [55] & 30.6 & 0.98 & 7.2 \\  & & [PERSON] [56] & 21.84 & 0.93 & 35.3 \\  & BMVC text & [PERSON] [57] & 19.57 & 0.89 & 53.0 \\  & & [PERSON] [58] & 22.27 & 0.92 & 50.6 \\  & & [PERSON] [63] & 22.56 & 0.95 & 10.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Defuzzification experimental results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Degradation** & **Dataset** & **Method** & **FM** & \(F_{PS}\) & **PSNR** & **DRD** \\ \hline \multirow{6}{*}{background texture} & & Otsu & 74.22 & 76.99 & 14.54 & 30.36 \\  & & [PERSON] & 41.12 & 41.57 & 6.67 & 91.23 \\  & & [PERSON] & 79.12 & 82.95 & 16.07 & 8.61 \\  & & [PERSON]’s UNet & 89.29 & 90.53 & 21.32 & 3.29 \\  & DIBCO & FD-Net & 95.25 & 96.65 & 22.84 & 1.22 \\ \hline \multirow{6}{*}{page smudge} & H-DIBCO & [PERSON]’s DSN & 88.04 & 90.81 & 18.94 & 4.47 \\  & & [PERSON]’s GiB & 83.16 & 87.72 & 16.72 & 8.82 \\  & & [PERSON]’s SAE & 79.22 & 81.12 & 16.09 & 9.75 \\  & & [PERSON]’s cGAN & 87.45 & 88.87 & 18.81 & 5.56 \\  & & [PERSON]’s wooConvCRF & 86.09 & 87.40 & 18.99 & 4.83 \\ \hline \multirow{6}{*}{handwriting fading} & & [PERSON] [35] & 59.25 & 59.18 & 11.80 & 9.56 \\  & & [PERSON] [36] & 88.34 & 90.37 & 19.11 & 4.93 \\ \cline{1-1}  & 2018 & DP-LinkNet & 95.99 & 96.85 & 22.71 & 1.09 \\ \cline{1-1}  & & [PERSON] [40] & 84.95 & 91.58 & 17.04 & 16.86 \\ \cline{1-1}  & & DocEnTr & 90.59 & 93.97 & 19.46 & 3.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of background texture, page smudging, and fading of lettering. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Degradation** & **Method** & \multicolumn{2}{c}{**Jung’s Datasets**} & \multicolumn{2}{c}{**RDSRD**} \\ \cline{3-6}  & & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline \multirow{6}{*}{poor lighting conditions} & [PERSON] [42] & 23.70 & 0.9015 & 28.24 & 0.8664 \\  & ST-CGAN & 23.71 & 0.9046 & 30.31 & 0.9016 \\ \cline{1-1}  & [PERSON] [4] & 24.45 & 0.8332 & 22.53 & 0.7056 \\ \cline{1-1}  & [PERSON] [44] & 28.49 & 0.9108 & 14.45 & 0.7054 \\ \cline{1-1}  & BEDSR-Net & 27.23 & 0.9115 & 33.48 & 0.9084 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of uneven illumination. For the background texture problem, it can be seen from the experimental results that the deep learning approach is more advantageous compared to the traditional thresholding approach; in particular, the FD-Net model effect is excellent in the comparison of these evaluation index data. As for the page smudging problem, it can be seen from the experimental results that the model effect using cGAN is better than the other four methods. For the experiments on handwriting fading, models specializing in antique documents, such as DP-LinkNet and DocEnTr, work better on the corresponding datasets. These two model structures are able to extract deeper semantic information. For the problem of uneven illumination, it is found by the results that the methods with better processing effects are those with more a priori conditions on the dataset, such as ST-CGAN and BEDSR-Net models, which use a dataset that requires three parts: shaded image, corresponding unshaded image, and shaded mask. The different datasets targeted by BEDSR-Net can also have better results. For the deblurring problem, most current algorithms are based on GAN structure changes to encode the blurred information accurately and achieve high-quality deblurring of images.