# A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging
## 3 Results  
The shadow removal results obtained for each tested method for HS images are displayed in Figure 6, where, for the sake of brevity, [PERSON] et al. 's method is referenced as SMGAN, [PERSON] et al. 's method is referenced as SynShadow, [PERSON] et al. 's method is referenced as Illumination Correction, and [PERSON] and [PERSON]'s method is referenced as Color transfer. It is evident that when shadow removal is performed in tested HS images, the results are hindered due to the high amount of land covered by such shadows. Likewise, it is noticeable that self-shadows typically found in trees and shrubs present a difficulty for color recovering. In Figure 6a-c, the results obtained in the Illumination correction method are displayed. It can be observed that the illumination correction algorithm loses color recovery accuracy when shadows cover asphalt and concrete; this can be seen as a bluish color in such regions. This fact makes shadow boundaries evident despite the boundary correction proposed in the tested work. Additionally, it is noticeable in the marked regions that green areas lose color accuracy and texture information. In Figure 6d-f, the results provided by the SMGAN method are depicted. It can be appreciated that this method keeps the texture features in the corrected regions but lacks an accurate color correction and narrows the image resolution to reduce the computational load. Figure 6g-i depicts the results for the color transfer method. It is shown that despite using a color transfer method based on the CIE L*a*b color space, the concrete and asphalt regions tend to keep the blue-like color or acquire a tone similar to other dominant regions in the unshadowed regions. The condition mentioned above is especially noticeable in Figure 6i in the region marked with a yellow rectangle, where the corrected shadows in asphalt become green-like. Additionally, in the grass regions highlighted with arrows, it can be noticed that such regions look blurred. In the case of Figure 6j-l, it can be observed that the SynShadow method shows visually accurate results that enhance the contour smoothing compared to traditional methods, but the corrected regions are still evident due to the chromatic differences found mainly in the asphalt areas, as signaled in the yellow rectangle. Lastly, corrected green areas are over lighted. Finally, Figure 6m-o depict the results of the proposed method. Although boundaries are still noticeable, it is discerned that the color in asphalt areas maintains an accurate visual consistency regarding the unshadowed one as marked in the yellow rectangles. In addition, it can be noticed that the grass areas highlighted keep texture information. As seen in Figure 6, HS images intricate the shadow removal task due to the limited information contained in unshadowed regions. In spite of this, the proposed method shows visual consistency for color and texture in grass and asphalt regions. The following set of results for SS images is shown in Figure 7.  
Figure 6: Illumination correction results for study cases 1 (**a**), 2 (**b**), and 3 (**c**); SMGAN method results for study cases 1 (**d**), 2 (**e**), and 3 (**f**); color transfer results for study cases 1 (**g**), 2 (**h**), and 3 (**i**); SynShadow method results for study cases 1 (**j**), 2 (**k**), and 3 (**l**); the proposed method results for study cases 1 (**m**), 2 (**n**), and 3 (**o**). As shown in Figure 7a-c, the illumination correction algorithm tends to depict blue-like colors in asphalt and concrete. In Figure 7d-f, the SMGAN method modifies the unshadowed areas, which represents a complete alteration of the image information. The results displayed in Figure 7g-i show that color correction in concrete and asphalt regions also tend to keep the blue-like color or acquire a tone similar to the other statistical dominant regions.