A Deep Network Architecture for Image Inpainting
文献类型：会议论文
| 作者 | Peng Xiang; Lei Wang; Jun Cheng; Bin Zhang; Jiaji Wu |
| 出版日期 | 2017 |
| 会议地点 | 中国成都 |
| 英文摘要 | A number of artworks have been damaged to some extent, over time, which greatly affect their visual quality. Therefore, it’s a very valuable and meaningful work to repair them. We proposed a deep network architecture—Image Inpainting Conditional Generative Adversarial Network (II-CGAN) to address this problem. Based on the deep convolutional neural network (CNN), we directly learn the mapping relationship between the damaged and repaired image detail layers from data. Since the intact image corresponding to the real-world damaged image is not available, we synthesize images with lost blocks for training. To minimize the lost of information and ensure better visual quality, a new refined network architecture is introduced. We made a thorough evaluation of the Generator of increased depth(22 layers) using an architecture with the units consisting of and convolution filters, and the Discriminator with small( ) convolution kernel used instead of in all convolution filters. Experiments results prove that the method in this paper achieves better objective and subjective performance. |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/11832]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2017 |
| 推荐引用方式 GB/T 7714 | Peng Xiang,Lei Wang,Jun Cheng,et al. A Deep Network Architecture for Image Inpainting[C]. 见:. 中国成都. |
入库方式： OAI收割
来源：深圳先进技术研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。