Name
Abstract | ||
|---|---|---|
Image inpainting has a good application value in image editing, however, traditional image inpainting techniques cannot complete semantic repair in the case of insufficient sample resources. Deep learning neural network have powerful learning capabilities and can extract high-level semantic features. These features can be used to semantically fill missing regions. Ideal image restoration needs to maintain structural consistency and texture clarity. In this paper, using the GAN network structure, we propose a inpainting method which constrains the repair process using the neighborhood loss function and gradient loss. The experimental results show that the repair results can maintain the global consistency of the structure and the clarity of the local texture. It shows that adding gradient loss constraint can further improve the quality of repair. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/FSKD.2018.8686914 | 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) |
Keywords | Field | DocType |
Image inpainting,semantic repair,Convolutional Neural Network(CNN) | CLARITY,Computer science,Image editing,Inpainting,Artificial intelligence,Generative grammar,Deep learning,Image restoration,Artificial neural network,Machine learning,Adversarial system | Conference |
ISBN | Citations | PageRank |
978-1-5386-8098-8 | 2 | 0.38 |
References | Authors | |
10 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liu Huaming | 1 | 3 | 2.77 |
| Guanming Lu | 2 | 29 | 9.43 |
| Bi Xuehui | 3 | 2 | 1.73 |
| Jingjie Yan | 4 | 4 | 3.14 |
| Weilan Wang | 5 | 9 | 11.75 |