# Diffusion-Denoising Process with Gated U-Net for High-Quality Document Binarization
## 4 Experiments and Results
### Quantitative and Qualitative Comparison
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Metric**} & **[PERSON]** & **[PERSON]** & **Competition** & **SAE** & **cGANs** & **[PERSON]** & **[PERSON]** & **[PERSON]** & \multirow{2}{*}{**Ours**} \\  & & **[6]** & **[5]** & **Winner** & **[28]** & **[15]** & **[30]** & **[17]** & **[18]** & **Ours** \\ \hline \multirow{4}{*}{2016} & FM & 86.64 & 79.57 & 88.72 & 88.11 & **91.67** & 90.48 & 84.45 & 91.11 & 88.95 \\  & pFM & 89.99 & 86.84 & 91.84 & 91.55 & 94.59 & 93.26 & 84.73 & 95.22 & **95.45** \\  & PSNR & 17.80 & 16.90 & 18.45 & 18.21 & **19.64** & 19.27 & 16.18 & 19.34 & 19.38 \\  & DRD & 5.52 & 6.76 & 3.86 & 4.51 & **2.82** & 3.94 & 7.25 & 3.25 & 3.74 \\ \hline \multirow{4}{*}{2017} & FM & 80.63 & 73.86 & **91.04** & 85.72 & 90.73 & 85.59 & 80.63 & 89.33 & 89.36 \\  & pFM & 80.85 & 84.78 & 92.86 & 87.85 & 92.58 & 87.56 & 80.85 & 91.41 & **94.02** \\  & PSNR & 13.84 & 14.30 & 18.28 & 16.09 & 17.83 & 16.39 & 13.84 & 17.91 & **18.33** \\  & DRD & 9.85 & 8.30 & **3.40** & 6.53 & 3.58 & 7.99 & 9.85 & 3.83 & 3.83 \\ \hline \multirow{4}{*}{2018} & FM & 51.56 & 64.04 & 88.34 & 75.77 & 87.73 & 76.51 & 77.59 & **91.86** & 88.43 \\  & pFM & 53.58 & 72.13 & 90.24 & 77.95 & 90.60 & 80.09 & 85.74 & **96.25** & 93.73 \\  & PSNR & 9.76 & 13.98 & 19.11 & 14.79 & 18.37 & 17.01 & 16.16 & **20.03** & 19.28 \\  & DRD & 59.07 & 13.96 & 4.92 & 13.30 & 4.58 & 8.11 & 7.93 & **2.60** & 3.95 \\ \hline \multirow{4}{*}{2019-B} & FM & 22.47 & 50.57 & 67.99 & 47.57 & 61.64 & 47.00 & 49.83 & 66.83 & **72.71** \\  & pFM & 22.47 & 54.48 & 67.88 & 48.55 & 62.52 & 47.60 & 49.97 & 68.32 & **75.24** \\  & PSNR & 2.61 & 10.85 & 12.14 & 10.84 & 11.77 & 9.18 & 8.55 & 12.91 & **14.37** \\  & DRD & 213.58 & 33.73 & 26.87 & 32.00 & 24.11 & 70.50 & 53.18 & 19.80 & **14.39** \\ \hline \multirow{4}{*}{Mean} & FM & 59.61 & 67.01 & 84.02 & 74.29 & 82.94 & 74.90 & 71.64 & 84.78 & **84.86** \\  & pFM & 61.53 & 74.56 & 85.71 & 76.47 & 85.07 & 77.13 & 71.85 & 87.80 & **89.61** \\ \cline{1-1}  & PSNR & 11.01 & 14.01 & 17.00 & 14.98 & 16.90 & 15.46 & 12.86 & 17.55 & **17.84** \\ \cline{1-1}  & DRD & 73.42 & 15.69 & 9.76 & 14.09 & 8.77 & 22.65 & 23.43 & 7.37 & **6.48** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the proposed model with other methods on the (H-)DIBCO dataset. The last row shows the results of the mean values on the four datasets. The best performance is indicated in bold, and the second-highest performance is underlined. The proposed model achieved the best or second-best performance in terms of the pFM and PSNR on all four datasets. Particularly, on the DIBCO 2019-B dataset, which consists of the most complex noise that the model has not yet experienced, the proposed model achieved the best performance across all metrics. In particular, the existing methods showed great decrement, whereas the proposed model showed impressive performance gains compared to the other models, even when faced with such significant noise. Although the quantitative differences between the proposed model and the other models were marginal on the other datasets, the qualitative evaluation demonstrated that the proposed model performed better in document binarization compared to the other models. Therefore, it demonstrated that training through the iterative diffusion-denoising process was effective in removing complex noise and robust to various environments. First, the proposed model delivered outstanding results across multiple benchmark datasets. In the H-DIBCO 2016 dataset, our model attained the highest performance in the pFM and the second-highest performance in the PSNR. The exceptional performance in the pFM underscores its superior ability to extract text strokes compared to the other methods, whereas the strong performance in the PSNR reflects the high quality of its results. Regarding the DIBCO 2017 dataset, our proposed model again took the lead, demonstrating the highest performance in both the pFM and PSNR.