Joint Blurred Image Restoration with Partially Known Information
Qing Wu, Xingce Wang, Ping Guo · 2006
A new restoration method for joint blurred images with partially known information is proposed in this paper. The joint blur 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