# A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging
## 3 Results
Figure 7j-l shows that shadow removal results present an accurate color correspondence, wherein corrected regions can be observed with slight over-illumination; this is especially noticeable in the regions signaled with a yellow rectangle. Regarding Figure 7j-l, the illumination correction results in the same color as the  
the results of the proposed method are depicted. It is noticeable that the color in the asphalt and concrete areas is recovered with visual accuracy, but in some corrected regions, it is observed that it is dark regarding unshadowed contiguous regions. Summarizing the results displayed in Figure 7, it is evident in the regions marked in a yellow rectangle that color consistency is improved in the proposed method, in which shadow boundaries are not as evident as the other traditional tested methods. Furthermore, it is quite remarkable that corrected self-shadows on the vegetation highlighted present an improvement in texture preservation. Likewise, it can be appreciated that self-shadows are also corrected. As seen in Figures 6 and 7, the proposed method delivers accurate visual results, in which color correction and texture preservation are the principal issues attended to in this work. In order to complement the quantitative analysis, visual analysis for the results obtained was realized. In Figure 8, the comparison on specific zones is realized. The analyzed zones are delimited with blue and red squares as depicted in Figure 8, in which the qualitative criteria to evaluate the shadow removal results are color correction and texture preservation. As observed in Figure 8b-g, the Illumination correction method provides results in which color and texture information are not visually consistent regarding unshadowed regions with similar land cover, and the texture information in green areas are blurred. In Figure 8c-h, a resolution loss due to the SMGAN method is noticeable. The end-to-end process also shows a poor shadow detection step in the study case analyzed. In the case of Figure 8d-i, it is evident that the texture information is accurately preserved. However, color information is visually inconsistent regarding unshadowed regions with the same land cover; specifically, Figure 8d shows over-saturated  
Figure 8: Input image for study case 1 (**a**), close view of the grass area for illumination correction (**b**), SMGAN method (**c**), color transfer method (**d**), SynShadow method (**e**), and the proposed work (**f**). A close view of asphalt color-corrected region for illumination correction (**g**), SMGAN method (**h**), color transfer method (**i**), SynShadow method (**j**), and the proposed work (**k**). colors with acceptable texture preservation, and the asphalt in Figure 8i displays bluish colors in the corrected regions. The SynShadow method results shown in Figure 8e-j depicts an over-illuminated correction in green areas (see Figure 8e), and the corrected asphalt regions show color inconsistency in the shadow boundaries, as shown in Figure 8e. Additionally, the image resolution is evidently reduced in the provided results. Figure 8f-k shows the results obtained with our proposed method; the color correspondence provided in the corrected asphalt regions (see Figure 8k) is improved compared to the other tested methods. Although shadow boundaries are still visible, the difference in terms of color between unshadowed and corrected regions is reduced regarding the rest of the methods. Moreover, as it is noticed in Figure 8f, green areas present an accurate texture and color when compared with the rest of works. It was seen in Figure 8 that the proposed work shows visually accurate results during qualitative analysis than the tested methods. As mentioned above, the quantitative analysis was executed by using SSDI, and Table 1 displays the results obtained in each study case for all methods compared. Table 1 presents the SSDI results computed for the methods tested.