Name
Abstract | ||
|---|---|---|
Existing image inpainting methods have shown their potential in filling corrupted regions with plausible contents. However, these methods tend to produce results with distorted structures or unnatural textures since they neglect the difference between structures and textures in images and jointly process these two different types of information. To solve this problem, we propose a bi-encoder network (BE-Net) that seeks to handle structure and texture information separately, and fuse them to reconstruct completed images. Specifically, BE-Net first uses two parallel encoders to infer structure and texture features of the input images respectively. Then a structure-texture consistency module (STCM) is designed to weaken artifacts and enhance visual coherency of the output images by keeping the texture features consistent with the structure features. Finally, the structure features and the texture features are fused at each level of the decoder to recover images with reasonable structures and realistic textures. Extensive experiments on Paris Street-View and CelebA datasets show the proposed approach is effective in generating realistic and visually plausible results and outperforms several state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9534475 | 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
Keywords | DocType | ISSN |
Image Inpainting, Bi-encoder Network, Structure-texture Consistency | Conference | 2161-4393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chujun Qin | 1 | 0 | 0.68 |
| Zhilin Huang | 2 | 0 | 0.68 |
| Ruixin Liu | 3 | 2 | 3.41 |
| Zhenyu Weng | 4 | 6 | 3.85 |
| Zhu Yuesheng | 5 | 112 | 39.21 |