  Image inpainting has achieved great advances by simultaneously leveraging
image structure and texture features. However, due to lack of effective
multi-feature fusion techniques, existing image inpainting methods still show
limited improvement. In this paper, we design a deep multi-feature co-learning
network for image inpainting, which includes Soft-gating Dual Feature Fusion
(SDFF) and Bilateral Propagation Feature Aggregation (BPFA) modules. To be
specific, we first use two branches to learn structure features and texture
features separately. Then the proposed SDFF module integrates structure
features into texture features, and meanwhile uses texture features as an
auxiliary in generating structure features. Such a co-learning strategy makes
the structure and texture features more consistent. Next, the proposed BPFA
module enhances the connection from local feature to overall consistency by
co-learning contextual attention, channel-wise information and feature space,
which can further refine the generated structures and textures. Finally,
extensive experiments are performed on benchmark datasets, including CelebA,
Places2, and Paris StreetView. Experimental results demonstrate the superiority
of the proposed method over the state-of-the-art. The source codes are
available at https://github.com/GZHU-DVL/MFCL-Inpainting.
