# Shadow Removal for Enhanced Nighttime Driving Scene Generation
## 3 Proposed Method
### Nighttime Translation  
For nighttime image transformation, TSIT [40] and CoMoGAN [42] models were employed. TSIT demonstrated high performance through (1) a multi-scale feature normalization scheme (FADE and FAdaIN) that captures coarse-to-fine structure and style information, and (2) a two-stream network design that effectively integrates both content and style. CoMoGAN proposed a novel model-guided setting for continuous I2I transformation, introducing an unsupervised framework with simple model guidance and a novel FIN layer. However, existing I2I translation models do not fully account for detailed features, such as shadows, when converting daytime driving scenes into nighttime scenes. As shown in Figure 4, distinct shadows are characteristics primarily found in clear, sunny daytime images, making it easy to detect whether a transformed image is fake based on the presence of shadows. By removing shadows from the input images, the model can generate more realistic nighttime images. The closer the augmented data are to real-world conditions, the more effective the data augmentation becomes, which necessitates the shadow removal process. For the nighttime transformation using TSIT, shadow-removed images produced by applying DHAN [10] and Partitioned Shadow Removal were used as the content images, while nighttime images from the BDD100K [61] dataset served as the source images. The pretrained model used for the transformation was TSIT's mmis_sunny2 diffweathers model, which is trained to transform content images across different weather conditions and times of day. The test_mode parameter allows for the selection of specific weather or \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**(a) Original**} & \multicolumn{2}{c}{**(b) Without**} & \multicolumn{1}{c}{**(c) With**} \\  & & & **Stitching** & **Stitching** \\ \hline \multirow{3}{*}{1} & upper region (blue) & 91.33 & 153.30 & 146.00 \\  & lower region (red) & 79.12 & 102.27 & 143.14 \\ \cline{2-5}  & **difference** & **12.21** & **51.03** & **2.86** \\ \hline \multirow{3}{*}{2} & upper region (blue) & 113.64 & 144.59 & 119.40 \\  & lower region (red) & 102.83 & 116.29 & 116.86 \\ \cline{1-1} \cline{2-5}  & **difference** & **10.81** & **28.30** & **2.54** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Contrasting average Y-channel values in Figure 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{} & \multicolumn{2}{c}{**Baseline**} & \multicolumn{3}{c}{**Proposed Method**} \\ \hline \multirow{2}{*}{**Hardware**} & \multirow{2}{*}{**DHAN**} & \multirow{2}{*}{**SpA-**} & \multirow{2}{*}{**DC-**} & \multirow{2}{*}{**ShadowNet**} & \multirow{2}{*}{**DHAN**} & \multirow{2}{*}{**SpA-**} & \multirow{2}{*}{**DC-**} \\  & & & **Former** & & & **Former** & **ShadowNet** \\ \hline \multirow{2}{*}{CPU} & 29.34 & 198.21 & 0.41 & 98.86 & 455.44 & 3.784 \\ \cline{1-1} \cline{2-5}  & - & 0.011 & 0.015 & - & 0.103 & 0.153 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Computational time for generating a shadow-free image (seconds). time conditions, and in this case, it was set to _night_. All other parameters were kept as recommended in the repository of the author to ensure consistency. Similarly, for the nighttime translation using CoMoGAN, shadow-removed images generated from DHAN and Partitioned Shadow Removal were used as input. The provided pretrained model was utilized without any modifications, and the nighttime transformation intensity, represented by the parameter \(\phi\), was set to 2.5 within the range of 0 to 3.14. The parameter \(\phi\) determines the level of darkness in the images, and the authors intended it to represent the angle of the sun. While any value could theoretically be chosen, setting it too high may cause objects to be obscured due to the nighttime effect, which would reduce consistent reliability of the segmentation results in the Experiment and Discussion section.