Self-Supervised Image Harmonization via Holistic Feature Fusion

Image harmonization is crucial for image composition, aiming to adjust the appearance of the foreground objects to be visually consistent to the background image, so as to produce a realistic yet natural composite image. Due to the difficulty in collecting large-scale annotated datasets, investigating self-supervised image harmonization methods has become a trend, while existing self-supervised methods typically struggle with complex cases with large visual discrepancy between the foreground and background. To address their limitations, we in this paper present a novel self-supervised framework for image harmonization. To allow for semantic-aware localized style adjustment and also global lighting transfer, we propose to perform holistic feature fusion, where local attention feature and global style feature are fused to produce the harmonization result. Experiments demonstrate that our method outperforms existing self-supervised image harmonization methods.