Shadow Removal

Shadow removal aims to computationally restore image regions obscured by shadows, achieving uniform illumination and recovering original colors and textures. Recent research heavily utilizes deep learning, employing transformer-based architectures, diffusion models, and U-Nets, often incorporating techniques like attention mechanisms and multi-scale feature extraction to handle the complex and varied nature of shadows. These advancements improve the accuracy and efficiency of shadow removal, particularly addressing boundary artifacts and inconsistencies between shadowed and non-shadowed areas. The impact extends to various applications, including computer vision, remote sensing, and digital photography, enhancing image quality and enabling more robust analysis of visual data.