# Diffusion-Denoising Process with Gated U-Net for High-Quality Document Binarization
## 4 Experiments and Results
### Ablation Study  
We conducted ablation experiments to assess each component of the proposed model. For evaluation purposes, we employed the DIBCO 2016 benchmark dataset and demonstrated the effectiveness of the proposed model using the four metrics outlined in Section 4.2. The baseline reference was [20], and the diffusion-denoising process was executed in the latent space using an autoencoder. We compared the baseline with the proposed gated U-Net and pixel-level loss individually, as well as models incorporating all of these components. In total, we compared four models, conducting experiments under identical implementation settings except for the inclusion or exclusion of specific components within each model. Table 2 shows the results of the baseline, proposed model without \(L_{pix}\), proposed model without gated U-Net, and proposed model. When the gated U-Net was added to the baseline, the pFM (95.03) improved by 0.59 compared to the baseline (94.44). Text-stroke preservation performance was improved because filter training for the gating values was additionally included. However, when the pixel-level loss was not included, the feedback for the gated convolution filter was not effective; thus, there were no significant performance improvements. When the gated convolution was omitted, both the text and background  
Figure 10: Binarization results of small text with degradation. (**a**) Degraded image, (**b**) ground-truth image, (**c**) cGANs [15], (**d**) [PERSON] et al. [30], (**e**) [PERSON] et al. [18], (**f**) the proposed model. The proposed model could effectively preserve the text region, even for small text. Figure 9: Binarization results of challenging images from the DIBCO 2019-B dataset. (**a**) Degraded image, (**b**) ground-truth image, (**c**) cGANs [15], (**d**) [PERSON] et al. [18], and (**e**) the proposed model. regions were updated solely through the pixel-level loss, leading to an improvement in performance across all metrics. Specifically, in the case of the proposed model with the pixel-level loss incorporated to train the gated convolution filter in the gated U-Net, we observed improvements in the FM, pFM, PSNR, and DRD of 0.68, 1.00, 0.28, and 0.31, respectively, compared to the baseline. This highlights the effectiveness of each individual component in the proposed model, and it also underscores the synergy of all components working together to achieve performance gains. Furthermore, to assess the impact of each component, we present the results of model training after six epochs in Table 3. We used the FM to evaluate the accuracy of the pixel-wise predictions for the text and background, along with the pFM to assess predictions at a regional level. When excluding the pixel-level loss from the proposed model, each metric remained comparable to or slightly improved upon the baseline performance. For the proposed model without the gated U-Net, which used the baseline's backbone network with an LDM and pixel-level loss to update the denoising network, we observed significant improvements in the FM of 8.00 and the pFM of 7.94 compared to the baseline. Conversely, the proposed model using the gated U-Net for document binarization and the pixel-level loss to facilitate the training of the gating values achieved the highest performance across all metrics, where the FM increased by 8.22 and the pFM increased by 8.51 compared to the baseline. These outcomes underscore the effectiveness of the individual components in the proposed model, particularly in terms of preserving and extracting text strokes. Figure 11 shows the resulting images using the baseline, the proposed model without \(L_{pix}\), the proposed model without the gated U-Net, and the proposed model. As shown in Figure 11f, the proposed model with both \(L_{pix}\) and the gated U-Net was successful in distinguishing between the text and background of degraded document images compared to the baseline shown in Figure 11c. Furthermore, in Figure 12, the excellent text-stroke preservation performance can be qualitatively confirmed.