A Novel Data and Model Hybrid-Driven Method for Image Restoration Using Residual Dense Attention U-Net
文献类型：会议论文
| 作者 | Ku T(库涛)1,3 ; Yang QR(杨琦瑞)1,2,3; Li J(李进)1,2,3; Liu JX(刘金鑫)1,3; Li DB(李殿博)1,3
|
| 出版日期 | 2021 |
| 会议日期 | May 28-30, 2021 |
| 会议地点 | Nanchang, China |
| 关键词 | under-display cameras image restoration attention RDAU-Net |
| 页码 | 305-311 |
| 英文摘要 | As people's pursuit of large screen-to-body ratio screen experience continues to improve, neither the digging front camera nor the bangs front camera can meet people's requirements for the front camera of a mobile phone. Therefore, the research and development of full-screen equipment has become a new trend. A full-screen device requires the imaging device to be placed below the screen, which we call an under-display cameras. The under-display cameras will improve the user's interactive experience while expanding the screen-to-body ratio of the mobile phone. However, there are many problems in the development of under-display cameras. When the imaging device is installed under the screen, the lower light transmittance will cause serious image degradation. Therefore, a new U-Net, which we call residual dense attention UNet (RDAU-Net), is proposed in this paper. A residual dense attention module which we propose in RDAU-Net to replace the single-layer convolution in the U-Net network. Meanwhile, the introduction of channel attention can effectively enhance the interdependence between channels, thereby adaptively re-dividing channel features. Experiments show that our RDAU-Net has better accuracy and faster recovery efficiency than existing methods. |
| 产权排序 | 1 |
| 会议录 | Proceedings - 2021 36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-6654-3712-7 |
| 源URL | [http://ir.sia.cn/handle/173321/29412]  |
| 专题 | 沈阳自动化研究所_数字工厂研究室 |
| 通讯作者 | Yang QR(杨琦瑞) |
| 作者单位 | 1.Shenyang Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences， Shenyang, China 3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China |
| 推荐引用方式 GB/T 7714 | Ku T,Yang QR,Li J,et al. A Novel Data and Model Hybrid-Driven Method for Image Restoration Using Residual Dense Attention U-Net[C]. 见:. Nanchang, China. May 28-30, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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