# Document Image Shadow Removal Based on Illumination Correction Method
## 4 Experiments
### Ablation Experiment  
To gain a deeper understanding of the role of each module in DICNet, a series of ablation experiments are conducted on the Adobe and HS datasets, and the results are shown in Table 5.  
First, the SSCA is removed and convolutions are used instead. The experimental results in the third line show that the addition of SSCA is very important to improve the model performance. Specifically, on the Adobe dataset, PSNR increases from 28.2369 to 33.0658, and RMSE decreases from 10.5231 to 5.8785. More notably, on the HS dataset, SSIM increases from 0.9413 to 0.9959. This demonstrates the important role of SSCA in fusing global channel information (illumination distribution) with local spatial information (text/background boundary). Especially on the HS dataset, the SSIM decreases from 0.9959  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **Adobe** & **HS** & **[PERSON]** & **[PERSON]** & **OSR** & **Average** & **Standard Deviation** \\ \hline ISR [17] & 5.7007 & 12.2114 & 14.1089 & 8.6179 & 10.7413 & 10.2760 & 2.9098 \\
3D-PC [15] & 15.0667 & 20.9485 & 19.5096 & 21.3490 & 18.1030 & 18.9954 & 2.2737 \\ WF [14] & 10.2377 & 22.3817 & 22.8450 & 16.6868 & 18.3221 & 18.0947 & 4.5762 \\ BE [31] & 13.6516 & 16.6430 & 9.0183 & 16.3216 & 16.5030 & 14.4275 & 2.9211 \\ LGBC [30] & 28.3047 & 19.8316 & 21.8917 & 28.6566 & 20.9347 & 23.9239 & 3.7789 \\ JWF [29] & 21.3437 & 16.8070 & 13.6268 & 14.5352 & 19.7622 & 17.2150 & 2.9583 \\ BEATE [28] & 32.2547 & 29.1951 & 24.6339 & 30.3406 & 24.5006 & 28.1850 & 3.1117 \\ MS-GAN [27] & 24.7240 & 24.9514 & 18.5062 & 19.9905 & 20.9466 & 21.8237 & 2.5819 \\ DCShadow-Net [25] & 27.9549 & 24.3020 & 18.2546 & 19.1632 & 23.3857 & 22.6121 & 3.5463 \\ CBENet [22] & 22.5846 & 24.5800 & 21.4163 & 28.2218 & 22.8219 & 23.9249 & 2.3748 \\ DICNet & 33.0658 & 30.7542 & 28.0092 & 31.1705 & 28.8094 & 30.3618 & 1.7924 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The quantitative analysis on PSNR.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Adobe**} & \multicolumn{3}{c}{**HS**} \\ \cline{2-7}  & **PSNR** & **SSIM** & **RMSE** & **PSNR** & **SSIM** & **RMSE** \\ \hline DICNet w/o SSCA & 28.2369 & 0.9821 & 10.5231 & 29.4675 & 0.9413 & 9.6438 \\ DICNet w/o mask & 30.6627 & 0.9913 & 7.5681 & 30.6781 & 0.9474 & 7.4896 \\ DICNet & 33.0658 & 0.9978 & 5.8785 & 30.7542 & 0.9959 & 7.4634 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The ablation experiment results.  
to 0.9413, which verifies the ability of SSCA to capture the details of the hard shadow transition area.  
Second, the shadow detection branch in the network structure is removed, and the network outputs the shadow-free images without a shadow mask. We can see that, compared to the complete DICNet, PSNR and SSIM decreased, and RMSE increased on both the Adobe and HS datasets. This indicates that the accurate soft shadow mask (**M**) serves as a \"navigation map\" for illumination correction: without **M**, the correction would blindly enhance all low-brightness regions (including non-shadow dark text), leading to a decrease in the contrast between text and background; whereas the presence of **M** ensures that the correction only acts on real shadow regions, preserving the original brightness of the text.