Image registration using PCA and Gradient Method for Super-resolution Imaging

摘要：

Super-resolution (SR) enhancement from multi-frame low-resolution (LR) images (multi-frame super-resolution) has been a well-studied topic in the literature.Image registration is the most important part for multi-frame super-resolution, and accurate alignment of LR images would contribute a critical role for the final success of SR image reconstruction. In this paper, we propose to combine the Principle Component Analysis (PCA) based registration method, which can perform object alignment in real-time and without constraints on the three registration parameters (i.e., translation, rotation, and scaling), and gradient registration method, which can perform precise registration with minor image movement. Experimental results show that the reconstruction SR images by our proposed method have much higher quality than those by the state of art algorithms.

关键词： super-resolutiont image nlignment principal component analysis gradient method

作者: So Sasatani Xian-Hua Han Yen-Wei Chen

作者单位: Graduate School of Information Science and Engineering, Ritsumeikan University Shiga, Japan

会议类型: 国际会议

会议名称: The 2nd International Conference on Software Engineering and Data Mining(IEEE 第二届国际软件工程和数据挖掘学术大会 SEDM 2010)

会议地点: 成都

会议语种:英文

页码: 599-602

在线出版日期: 2010-06-23（万方平台首次上网日期，不代表论文的发表时间）