Shadow-aware image colorization

Abstract

Significant advancements have been made in colorization in recent years, especially with the introduction of deep learning technology. However, challenges remain in accurately colorizing images under certain lighting conditions, such as shadow. Shadows often cause distortions and inaccuracies in object recognition and visual data interpretation, impacting the reliability and effectiveness of colorization techniques. These problems often lead to unsaturated colors in shadowed images and incorrect colorization of shadows as objects. Our research proposes the first shadow-aware image colorization method, addressing two key challenges that previous studies have overlooked: integrating shadow information with general semantic understanding and preserving saturated colors while accurately colorizing shadow areas. To tackle these challenges, we develop a dual-branch shadow-aware colorization network. Additionally, we introduce our shadow-aware block, an innovative mechanism that seamlessly integrates shadow-specific information into the colorization process, distinguishing between shadow and non-shadow areas. This research significantly improves the accuracy and realism of image colorization, particularly in shadow scenarios, thereby enhancing the practical application of colorization in real-world scenarios.