Facial Image Shadow Removal via Graph-based Feature Fusion

Abstract

Despite natural image shadow removal methods have made significant progress, they often perform poorly for facial image due to the unique features of the face. Moreover, most learning-based methods are designed based on pixel-level strategies, ignoring the global contextual relationship in the image. In this paper, we propose a graph-based feature fusion network (GraphFFNet) for facial image shadow removal. We apply a graph-based convolution encoder (GCEncoder) to extract global contextual relationships between regions in the coarse shadow-less image produced by an image flipper. Then, we introduce a feature modulation module to fuse the global topological relation onto the image features, enhancing the feature representation of the network. Finally, the fusion decoder integrates all the effective features to reconstruct the image features, producing a satisfactory shadow-removal result. Experimental results demonstrate the superiority of the proposed GraphFFNet over the state-of-the-art and validate the effectiveness of facial image shadow removal.