# Image Shadow Removal Using End-To-End Deep Convolutional Neural Networks
## 4 Experiment
### Performance Comparison Analysis
The algorithm proposed by [PERSON] [21] had a better overall effect than the other four methods without deep learning, however, when comparing the shadow removal effect map obtained by the fully automatic RSnet model proposed in this paper, it can be seen that RSnet's shadow-free image quality was higher, especially for the detail processing, which was far better than the interactive method proposed by [PERSON] [21]. Observing the last three columns in Figures 3-5, it can be found that the depth learning-based methods proposed by [PERSON] [30] and [PERSON] [31] and the RSnet algorithm proposed in this paper were better than the previous ones. It reflected that the overall effect of deep learning applied to image shadow removal was better. It can be clearly seen from the fifth column in Figures 3-5 that the bilateral filtering-based algorithm proposed by [PERSON] [28] changed the non-shaded area. Of course,  
Figure 4: Schematic diagram of shadow removal in complex scenes. for the first and second rows of the fifth column in Figure 3, the image reconstruction effect was good, the reconstruction effect of the non-shadow region was good, and the non-shadow region was not significantly changed. However, the effect of shadow removal was not good enough, and the shadow area could still be observed more clearly from the shadow-free image after removal. At the same time, using these two images to compare the shadow-free image obtained by the Rsnet model proposed in this paper, it can be seen that the latter removal effect was better in both the non-shadow area and the shadow area, and the obtained shadow-free image quality was higher. Of course, for the shaded areas in the third and last lines, the algorithm proposed in this paper did not perform preferably on the removal of shadow areas, but still had advantage over other algorithms, especially for the detailed processing of edge information. Figure 4 mainly shows the shadow removal effect of different algorithms under complex scene conditions. A comprehensive analysis of Figures 3 and 4 shows that some algorithms had better shadow removal effects in simple scenes, but the shadow removal effect in complex scenes was relatively poor. These algorithms of the overall analysis and comparison show that the quality of the shadow-free image obtained in the simple scene was higher than that of the shadow-free image obtained in the complex scene. Therefore, it can be explained that these algorithms had poor stability and could not adapt to shadow images with multiple semantic inputs at the same time. When the texture was complex and the shadows were small, these algorithms could not obtain high-quality, shadow-free images. Of course, the two methods based on deep learning were more stable than the other five algorithms. However, when comparing the Rsnet model, it can be found that [PERSON] [30] had a poor effect on small shadows, and [PERSON] [31] had a poor shadow removal effect on complex scenes, such as grass. The Rsnet model had excellent shadow removal effects for both scenarios, and both had high quality, shadow-free images. Analysis of lines 4 and 5 in Figure 4 revealed that when shadows were projected onto a wall with complex textures, [PERSON] [21], [PERSON] [15], [PERSON] [13], [PERSON] [28], and Finlayson [22] did not get high-quality, shadow-free images, and the shadow removal effect was not ideal. The shadow-free image quality obtained by the Rsnet network in this case was relatively higher, and the shadow removal effect was better, even equivalent to the true shadow-free image in the second column. The last line in Figure 4 shows the shadow removal effect of different algorithms when people and bicycles were used as projection objects. This was a common shadow image in our daily life, but the shadow removal methods of the other models were not satisfactory. In particular, the more complicated and fine shadow processing effects on the bicycle tire was extremely poor, and the corresponding  
Figure 5: Schematic diagram of shadow removal in special scenes.