# Shadow Removal for Enhanced Nighttime Driving Scene Generation
## 1 Introduction  
Autonomous driving systems heavily rely on the performance of vision systems, which play a critical role not only in ensuring safety and efficiency during well-lit daytime conditions but also in addressing the complex challenges posed by nighttime driving environments, such as reduced visibility, varied lighting conditions, and sensor noise. Current learning-based approaches are sensitive to the datasets they are trained on, leading to significant performance degradation when models trained exclusively on daytime datasets are applied to nighttime scenarios. This performance drop arises because existing datasets often fail to adequately capture the characteristics of nighttime environments. Moreover, most publicly available datasets today are biased towards daytime conditions. To address this issue, there has been a growing body of research employing generative models to augment data and develop models robust to exceptional conditions, such as nighttime and adverse weathers [1, 2]. Furthermore, this approach allows for the reuse of a single image multiple times without requiring repeated labeling. While Image-to-Image (I2I) translation [3] has shown promise in addressing dataset biases, particularly towards daytime scenes, existing methods often fail to capture the fine-grained details of nighttime environments, such as subtle lighting variations and shadow characteristics. In nighttime driving scenes, shadows should be faint or absent, yet synthesized images often retain pronounced shadows, making them easily identifiable as synthetic. To overcome this limitation, we propose a novel approach that combines shadow removal with I2I translation. Our enhanced shadow removal method, which is applicable to full-context images like those found in driving datasets, is integrated into the I2I translation pipeline. Recent shadow removal methods [4; 5; 6; 7] are predominantly learning-based. However, the datasets commonly used for training these models, such as ISTD (Image Shadow Triplets Dataset) [8] and SRD (Shadow Removal Dataset) [9], lack diversity and primarily consist of local images, leading to poor recognition of shadow regions. Although previous research [10] has addressed this issue through data augmentation, achieving high segmentation performance in shadow regions, the method (DHAN) still struggles when applied to complex scenes, such as driving datasets, where objects and shadows of varying depths coexist within a single image. To overcome these limitations, we propose a novel approach that divides the image into multiple regions, thereby reducing the complexity caused by variations in depth and heterogeneity. In driving scenes, objects and shadow regions in the central area tend to be smaller due to differences in depth. When processing the entire image at once, the shadow removal performance is particularly limited in this central region. By dividing the image and passing each partition through a shadow removal model, we generated shadow-free images for each partition, which were then reassembled. However, without preprocessing, the brightness differences between the separately processed region can create visual inconsistencies when merging them back. To address this limitation, we applied image stitching to generate a seamless final one. Our experimental results demonstrate that this method is capable of synthesizing data more suited for nighttime conditions. Training a segmentation model on the dataset generated after shadow removal and nighttime translation, and testing it on a real-world nighttime dataset, showed improved segmentation performance, particularly in road classes where shadows are prevalent. Therefore, our approach enables the generation of more realistic nighttime datasets by better capturing the intricate details of nighttime driving scenes. In summary, the contributions of this paper are as follows:  
* The development of a novel hybrid pipeline that combines shadow removal with I2I translation to generate more realistic nighttime driving scenes.