# A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging
## 1 Introduction
[35] to perform the shadow detection and removal process, where they proposed a SynShadow model, a large-scale dataset of shadow/shadow-free/matte image triplets, and the pipeline to synthesize the diverse and realistic triplets. An adversarial neural network (ANN) based solution has been recently proposed by [PERSON] et al. [36]. The authors targeted obtaining a shadow detection and removal procedure by taking care of the image color consistency at the mask silhouette region. This problem is tackled using the multi-scale and global feature (MSGF) and the direction feature (DF) algorithms, resulting in an improvement in the balance error rate (BER) index for shadow detection and the root mean square error (RMSE) index for shadow removal when compared to ground-truth images for the image shadow triplets dataset (ISTD) and Stony Brook University (SBU) public datasets. ANN solutions require a comprehensive dataset of training images, sometimes produced by a manual image transform process that could require experts' time. For a shadow removal ANN, shadow mask creation could be performed manually or in an automated fashion with a specific algorithm. For the case of GAN-based solutions, such as the one published by [PERSON] et al. [37], the training process is described as a semi-supervised task, which does not rely as heavily on supervised data to set its model parameters; this solution uses a multi-step coarse-to-fine-grained shadow removal deep learning image generation process. However, solutions based on the deep learning processing scheme require a large amount of computing power for the training process, as the number of generations, the training dataset image count, and the input data size is substantially large. In addition to the aforementioned, pre-established image input and output sizes exist for the ANN models, requiring a resizing at both ends for effectively computing the shadow detection and removal processes. Since complex scenarios could involve a heterogeneous mix of materials and textures, algorithms need to adapt themselves for any case. The study presented by [PERSON] et al. [38] tries to solve this issue by two key steps: the first step is devoted to a filtering process where the textures are taken away, while the second and last step effectively incorporates depth cue data to the preprocessed input. This method shows an advantage for its second step, as no extra information or specific purpose hardware, such as a LiDAR capture device, is required, reducing the implementation cost and enabling the ability to handle more scenarios. Aerial imaging-related works are some of the emerging issues in the shadow removal task, in which the methods often base their work on illumination feature correction [14] and local color transfer [39]. Notwithstanding, the applications in UAV imaging still lack accurate results in color-corrected and texture-preserving shadows; this is mainly noticeable in the case of urban scenes that often contain several heterogeneous regions. Additionally, the capturing of aerial images take place at outdoor scenes, where two light sources mainly illuminate the environment: a light source (the sun) and a diffuse source (the sky) with different spectral power distributions; the skylight has components in the wavelengths from 450 to 495 nm of the visible spectrum [40]. Shadows are perceived when the direct illumination of the sun is blocked, and a region is only illuminated by the light of the sky; all regions covered by shadows appear to be more bluish. This phenomenon is especially noticeable in regions that present low saturated colors when illuminated by sunlight (concrete and asphalt); images captured with standard cameras also tend to capture images in which the bluish regions are visually and numerically discerned [41]. In this work, we present a novel method for shadow removal based on the characteristics of the CIE L*a*b (luminance, a, and b) color space and the HSV (Hue, Saturation, and Value) color space.