Abstract:
Image inpainting is a reconstruction method, where a
corrupted image consisting of holes is filled with the most relevant
contents from the valid region of an image. To inpaint an image,
we have proposed a lightweight cascaded architecture with 2.5 M
parameters consisting of encoder feature aggregation block (FAB)
with decoder feature sharing (DFS) inpainting network followed
by a refinement network. Initially, the FAB with DFS (inpainting)
generator network is proposed which comprises of multi-level
feature aggregation mechanism and feature sharing decoder. The
FAB makes use of multi-scale spatial channel-wise attention to fuse
weighted features from all the encoder levels. The DFS reconstructs
the inpainted image with multi-scale and multi-receptive feature
sharing in order to inpaint the image with smaller to larger hole
regions effectively. Further, the refinement generator network is
proposed for refining the inpainted image from the inpainting
generator network. The effectiveness of proposed architecture is
verified on CelebA-HQ, Paris Street View (PARIS_SV) and Places2
datasets corrupted using publicly available NVIDIA mask dataset.
Extensive result analysis with detailed ablation study prove the robustness of the proposed architecture over state-of-the-art methods
for image inpainting.
