# Document Image Shadow Removal Based on Illumination Correction Method
## 4 Experiments
### Quantitative Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **Adobe** & **HS** & **Jung** & **Kligler** & **OSR** & **Average** & **Standard Deviation** \\ \hline ISR [17] & 0.6511 & 0.8707 & 0.8714 & 0.7544 & 0.8259 & 0.7947 & 0.0835 \\
3D-PC [15] & 0.7931 & 0.8970 & 0.8527 & 0.7969 & 0.8491 & 0.8378 & 0.0388 \\ WF [14] & 0.7748 & 0.9370 & 0.9173 & 0.8341 & 0.9141 & 0.8755 & 0.0614 \\ BE [31] & 0.7182 & 0.8608 & 0.6576 & 0.7955 & 0.8234 & 0.7711 & 0.0736 \\ LGBC [30] & 0.8581 & 0.7093 & 0.8135 & 0.8841 & 0.8256 & 0.8181 & 0.0598 \\ JWF [29] & 0.9169 & 0.9072 & 0.8747 & 0.8023 & 0.9004 & 0.8803 & 0.0414 \\ BEATE [28] & 0.9513 & 0.9469 & 0.9162 & 0.9268 & 0.9372 & 0.9357 & 0.0129 \\ MS-GAN [27] & 0.8506 & 0.9225 & 0.8816 & 0.8160 & 0.9117 & 0.8765 & 0.0393 \\ DCShadow-Net [25] & 0.9183 & 0.9351 & 0.8778 & 0.8267 & 0.8996 & 0.8915 & 0.0376 \\ CBENet [22] & 0.9249 & 0.9566 & 0.9466 & 0.9216 & 0.9506 & 0.9401 & 0.0141 \\ DICNet & 0.9978 & 0.9949 & 0.9894 & 0.9956 & 0.9942 & 0.9944 & 0.0028 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The quantitative analysis on SSIM. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **Adobe** & **HS** & **Jung** & **Kligler** & **OSR** & **Average** & **Standard Deviation** \\ \hline ISR [17] & 132.8236 & 69.9663 & 51.4320 & 94.9936 & 74.7759 & 84.7983 & 27.4869 \\
3D-PC [15] & 45.1189 & 22.9948 & 27.1410 & 22.1827 & 32.5690 & 30.0013 & 8.4088 \\ WF [14] & 79.1152 & 20.6632 & 18.4178 & 38.0803 & 31.9471 & 37.6447 & 21.9568 \\ BE [31] & 55.0093 & 41.5587 & 96.7136 & 40.5711 & 40.9424 & 54.9590 & 21.5708 \\ LGBC [30] & 10.3736 & 27.5048 & 20.8406 & 9.7618 & 23.3185 & 18.3599 & 7.1004 \\ JWF [29] & 23.7720 & 38.1403 & 53.7947 & 48.7566 & 28.1078 & 38.5143 & 11.5253 \\ BEATE [28] & 6.5083 & 9.0040 & 15.2094 & 8.0355 & 15.6698 & 10.8854 & 3.8055 \\ MS-GAN [27] & 15.6217 & 14.6334 & 31.1069 & 27.1857 & 23.9341 & 22.4964 & 6.4387 \\ DCShadow-Net [25] & 10.4625 & 15.7446 & 33.1704 & 29.7510 & 23.3857 & 22.5028 & 8.4588 \\ CBENet [22] & 24.7669 & 16.6032 & 22.0069 & 10.1808 & 19.6035 & 18.6323 & 5.0102 \\ DICNet & 5.8785 & 7.4634 & 10.1487 & 7.2563 & 9.3031 & 8.0100 & 1.5268 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The quantitative analysis on RMSE. From Tables 2 and 4, we can see that DICNet achieves the highest SSIM (0.9978) and PSNR (33.0658) on the Adobe dataset, far exceeding the suboptimal method BEATE (SSIM 0.9513, PSNR 32.2547). This may be because the shadow variations in the Adobe dataset are complex (each document has 5-9 shading variations), while the SSCABlock of DICNet can dynamically balance the global illumination distribution and local shadow details, avoiding the over-correction of BEATE in strong shadow regions. DICNet achieves the optimal performance in terms of PSNR (30.7542) and SSIM (0.9949) on HS dataset, which contains hard shadows and transition regions under sunlight and LED lighting. This is attributed to the soft shadow mask output by the shadow detection branch, which can accurately distinguish between the core area of hard shadows and the transition area. The illumination correction block supplements light in a graded manner based on this mask. In contrast, methods like MS-GAN, due to their rough masks, tend to produce brightness discontinuities in the transition area. Overall, the proposed method shows significant performance benefits on different document shadow removal tasks; it performed better in terms of structural detail preservation, error minimization, and overall image quality improvement.