# Image Shadow Removal Using End-To-End Deep Convolutional Neural Networks
## 4 Experiment
### Performance Comparison Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Database** & **Description** & **Fin.** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **Ours** \\  & & [22] & [28] & [13] & [15] & [21] & [30] & [31] & **Ours** \\ \hline \multirow{3}{*}{LRSS [16]} & Shadow/ & \multirow{3}{*}{0.758} & \multirow{3}{*}{0.8336} & \multirow{3}{*}{0.8507} & \multirow{3}{*}{0.8953} & \multirow{3}{*}{0.9580} & \multirow{3}{*}{0.9162} & \multirow{3}{*}{0.9587} & \multirow{3}{*}{**0.9772**} \\  & Output & & & & & & & & \\ \cline{2-2} \cline{6-8}  & Target/ & \multirow{2}{*}{0.8976} & \multirow{2}{*}{0.8549} & \multirow{2}{*}{0.9074} & \multirow{2}{*}{0.9496} & \multirow{2}{*}{0.9633} & \multirow{2}{*}{0.9780} & \multirow{2}{*}{0.9679} & \multirow{2}{*}{**0.9814**} \\  & Output & & & & & & & & \\ \hline \multirow{3}{*}{UIUC [13]} & Shadow/ & \multirow{3}{*}{0.7904} & \multirow{3}{*}{0.8549} & \multirow{3}{*}{0.9495} & \multirow{3}{*}{0.9423} & \multirow{3}{*}{0.9543} & \multirow{3}{*}{0.9676} & \multirow{3}{*}{0.9641} & \multirow{3}{*}{**0.9788**} \\  & Output & & & & & & & & \\ \cline{2-2} \cline{6-8}  & Target/ & \multirow{2}{*}{0.8336} & \multirow{2}{*}{0.8889} & \multirow{2}{*}{0.9226} & \multirow{2}{*}{0.9576} & \multirow{2}{*}{0.9773} & \multirow{2}{*}{**0.9868**} & \multirow{2}{*}{0.9801} & \multirow{2}{*}{0.9848} \\  & Output & & & & & & & & \\ \hline \multirow{3}{*}{UCF [42]} & Shadow/ & \multirow{3}{*}{0.8525} & \multirow{3}{*}{0.8651} & \multirow{3}{*}{0.8625} & \multirow{3}{*}{0.8931} & \multirow{3}{*}{0.9487} & \multirow{3}{*}{0.9121} & \multirow{3}{*}{0.9556} & \multirow{3}{*}{**0.9832**} \\  & Output & & & & & & & & \\ \cline{2-2} \cline{6-8}  & Target/ & \multirow{2}{*}{–} & \multirow{2}{*}{–} & \multirow{2}{*}{–} & \multirow{2}{*}{–} & \multirow{2}{*}{–} & \multirow{2}{*}{–} & \multirow{2}{*}{–} & \multirow{2}{*}{–} \\  & Output & & & & & & & & \\ \hline \multirow{3}{*}{RSDB (ours)} & Shadow/ & \multirow{3}{*}{0.8580} & \multirow{3}{*}{0.6904} & \multirow{3}{*}{0.7875} & \multirow{3}{*}{0.8609} & \multirow{3}{*}{0.8591} & \multirow{3}{*}{0.8953} & \multirow{3}{*}{0.9011} & \multirow{3}{*}{**0.9826**} \\  & Output & & & & & & & & \\ \hline \multirow{3}{*}{RSDB (ours)} & Target/ & \multirow{3}{*}{0.8124} & \multirow{3}{*}{0.6725} & \multirow{3}{*}{0.7704} & \multirow{3}{*}{0.9122} & \multirow{3}{*}{0.9063} & \multirow{3}{*}{0.9344} & \multirow{3}{*}{0.9532} & \multirow{3}{*}{**0.9910**} \\  & Output & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative results using structural similarity (SSIM) (the bigger the better), the maximum value is shown in bold. Figures 3-5 show the image shadow removal effects of different algorithms under simple semantics, complex semantics, and special semantics. The first column is the original image (shadow image), the second column is the real shadow-free image, and the last eight columns are the shadow removal images obtained by eight different algorithms. The last column is the shadow removal image obtained by the RSnet model proposed in this paper. It is worth noting that the first few lines of Figures 3 and 4 show the image shadow removal effect when the projection object is smaller and more trivial, and the last few lines show the shadow removal effect when the projection object is larger. Comparing the processing effects of these two different projection objects, it can be clearly found that when the projection object was larger and the shadow area was more concentrated, that is, the shadow occupied a large proportion of the entire image, the overall removal effect of the image shadow was much better than that of the trivial projection object, and had a smaller shadow area. Analyzing the image shadow removal effect diagram in Figure 3, we found that [PERSON] [22] and [PERSON] [21] had relatively stable results for shadow images in simple scenes; the former was better than the latter.