# A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging
## 4 Discussion  
As proven in the previous section, the shadow removal task executed over images containing urban aerial scenes still presents a challenging task. According to the experiments executed, the Illumination correction method presented noticeable bluish colors in corrected zones that include asphalt and concrete; this is especially visible in Figure 6a-c. This result is mainly caused by the lack of chromatic correction in the shadowed regions that include elements that present low saturated colors when illuminated by sunlight and are bluish when illuminated by a skylight. In the case of the Color transfer method, the chromatic correction is executed. However, as it is mainly observed in Figures 6i and 7i, the corrected shadows display a slight green color; this is caused during the color transfer process. The unshadowed low saturated regions are classified with the rest of the unshadowed colors contained in the image, which causes an ambiguous classification, leading to color transfer results with corrected regions that show a slight color tone similar to the dominant region (green color in the experiments performed). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test** & **Proposed** & **Illumination** & **Color** & **SMGAN** & **SynShadow** \\  & **Work** & **Correction**[25] & **Transfer**[33] & **[16]** & **[35]** \\ \hline
1 & 17.239 & 36.963 & 28.198 & 33.780 & 19.610 \\
2 & 20.953 & 22.396 & 20.579 & 31.980 & 17.155 \\
3 & 11.895 & 21.160 & 15.798 & 17.139 & 12.26 \\
4 & 13.145 & 19.458 & 22.242 & 13.971 & 13.81 \\
5 & 14.779 & 20.483 & 20.809 & 13.212 & 16.300 \\
6 & 12.900 & 23.484 & 21.866 & 18.852 & 13.288 \\ \hline AVG & 15.152 & 21.991 & 21.582 & 21.489 & 15.404 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results comparison of SSDI results. It can be seen in Figures 6d-f and 7d-f that the SMGAN method presents variated results, in which it is noticed that in the case of Figure 6f, the corrected regions show accurate color and texture results, but such results are not extended to the rest of the images. Additionally, in Figure 7d-e, it is observed that the method modifies the colors in the entire image. Lastly, the SynShadow method results are depicted in Figures 6j-l and 7j-l; it is evident that the SynShadow method performs acceptable boundary smoothing. Nevertheless, the color correction results are still visually perceptible; also, shadowed green areas correction tends to provide over-illuminated pixels, which can be mainly observed in Figure 6j,l, and detailed in Figure 8. Deep learning methods present an innovative and functional methodology that is able to execute such tasks as an end-to-end process; it can be discerned in that deep learning-based methods provide results that enhance the boundaries smoothing when it is visually compared to traditional methods. Nevertheless, the results can vary depending on the training process and the method development, and the computational load implies an image resizing that can lead to data loss. In addition, although the shadow boundaries smoothing is executed, the color correction results provided still keep visual evidence of the corrected regions. The results of this study's experiments are shown in Figures 6m-o and 7m-o. It was demonstrated through them that executing the color classification grouping for the regions that contain concrete and asphalt enhances the color correction results and avoids the statistical misclassification of such regions. It was also demonstrated that the dilation applied to the shadow mask improves the statistical relation between shadowed and unshadowed regions; this can be appreciated in improved texture preservation, especially in green areas, as detailed in Figure 8. The relatively low computational load allows this method to be executed over high-resolution images. Nonetheless, the boundaries smoothing is still deficient in most of the study cases tested; this opens the opportunity of improving the present results working in boundaries processing.