A Novel Data and Model Hybrid-Driven Method for Image Restoration Using Residual Dense Attention U-Net

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 数字工厂研究室

	A Novel Data and Model Hybrid-Driven Method for Image Restoration Using Residual Dense Attention U-Net
	Ku T(库涛)1,2; Yang QR(杨琦瑞)1,2,3; Li J(李进)1,2,3; Liu JX(刘金鑫)1,2; Li DB(李殿博)1,2
	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
内容类型	会议论文
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.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China 2.Shenyang Institute of Automation, Chinese Academy of Sciences 3.University of 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.