# Image Shadow Removal Using End-To-End Deep Convolutional Neural Networks
## 4 Experiment
### Database  
Shadow removal has important research significance. Researchers at home and abroad have been extensively studying this issue, especially in the past decade, but a database specifically constructed for this problem is very rare. Currently, the most widely used databases are presented by [PERSON] [13] and [PERSON] [16], but they only contain 76 and 37 pairs of shadow/shadow-free image pairs. In order to better evaluate the shadow removal algorithm proposed in this paper, we constructed a new RSDB database, which contained 2685 shadow and shadow-free image pairs. The comparison with other shadow removal databases in recent years is shown in Table 1.  
In order to better collect data and build a new shadow removal database, we used a tripod to fix the camera in the specified position and a wireless Bluetooth remote control unit to capture images during the shooting process to ensure that the shadow/shadow-free image was consistent with the background. Firstly, to set a fixed exposure compensation, focal length, and other parameters, the exposure compensation was set to 0, and the focal length was 4 mm; then, the shadow image projected by different objects was taken. Finally, the shadow cast by the object was removed, that is, the projection of the object was removed, and the background image of the corresponding shadow image was captured, that is, the image without shadow.  
In addition, to diversify the database, the new RSDB database had the following characteristics:  
Brightness: In order to capture shadows, databases with different softness, that is, shadow databases with different light intensities, included soft shadows and hard shadows. We collected shadow images at the same time in different weather conditions and different time periods in the same conditions. For example, we can take shadow pictures at the same time (including 11 non) on cloudy  
\begin{table}
\begin{tabular}{c c c c} \hline
**Database** & **Amount** & **Content of Images** & **Type** \\ \hline LRSS [16] & 37 & Shadow/Shadow-free & pair \\ UIUC [13] & 76 & Shadow/Shadow-free & pair \\ UCF [42] & 245 & Shadow/Shadow mask & pair \\ RSDB (ours) & 2685 & Shadow/Shadow-free & pair \\ \hline \end{tabular}
\end{table}
Table 1: Comparison table with other shadow databases.  
and sunny days, or we can choose 7:00 in the morning, 12 in the afternoon, and 6 in the afternoon at different times of the day. The data was collected to obtain a soft shadow and hard shadow database.  
Shape: In order to ensure the shape diversity of the shadow database, during the shooting process the shadows were projected by various shapes of objects such as school bags, umbrellas, bicycles, etc. In the meantime, the characteristics of subjective initiative are fully utilized, and the body was projected as a shadow. The object performed a multi-pose change and then projected it into a specified area, thus obtaining a shadow database with various shapes.  
Scenario: Multiple scenes were another requirement for shadow removal databases. Given this, we collected separate shadow images for different scenes in the process of shadow-collecting, such as grass, roads, campus, and walls.  
Reflection: When we were shooting a shadow database, we reflect the shadows of different objects to mirror the existence of shadows in real life more realistically. An example of an RSDB database is shown in Figure 2.  
Figure 2: Illustration of several shadow and shadow-free image pairs in the RSDB database. The (**a1**, **b1**, **c1**) columns show the shadow images (inputs) in different scenes. The (**a2**, **b2**, **c2**) columns indicate true shadow-free images (targets) corresponding to the (**a1**, **b1**, **c1**) columns.