JOINT BLURRED IMAGE RESTORATION WITH PARTIALLY KNOWN INFORMATION

摘要：

A new restoration method for joint blurred images with partially known information is proposed in this paper. The joint blur here is assumed to be motion blurs and defocus blur mixed together. Under the condition of two blur effects are supposed to be independent linear shift-invariant processes and motion blur parameter can be obtained with known information, a reduced update Kalman filter (RUKF) is used for degraded image restoration and the best defocus point spread function (PSF) parameter is determined based on the maximum entropy principle (MEP). Experimental results with real images show that the proposed approach works well.

关键词： PSF estimation Joint blurred image Reduced update Kalman filter Maximum entropy principle

作者: QING WU XING-CE WANG PING GUO

作者单位: Image Processing and Pattern Recognition Laboratory Beijing Normal University, Beijing 100875, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 3853-3858

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）