Consistent Fully Automatic Image Colorization Due to Bridging Fully Automatic and Point Interactive Models

Image colorization is a fundamental task in computer vision that aims to predict the missing color channels from grayscale images. In recent years, fully automatic approaches based on deep learning have become the dominant paradigm. However, these methods often produce visually unnatural results, such as color bleeding or inconsistent colorization in homogeneous regions. On the other hand, user interactive methods, such as point interactive colorization, propagate colors based on user-provided hints and tend to produce more natural and spatially consistent results. Nevertheless, when no hints are provided, the generated images may suffer from low color saturation. In this study, we propose a novel fully automatic colorization framework that combines the strengths of both paradigms: a conventional fully automatic colorization model is used as a hint generator, and a conventional point interactive colorization model is employed as a hint propagator. By treating the interactive model as a propagator within an automatic pipeline, our method ensures that the inherent colorfulness in automatic models is preserved while achieving the spatial consistency characteristic of interactive methods. Importantly, the proposed framework is fully automatic, requires no manual input, and does not necessitate retraining, as it can directly leverage existing pretrained models. We evaluated the proposed method using various fully automatic colorization models and a representative point interactive model. The results demonstrate that our method effectively reduces color inconsistencies in continuous regions and improves visual realism.