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Joint Blurred Image Restoration with Partially Known Information

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3 Author(s)
Qing Wu ; Image Processing and Pattern Recognition Laboratory Beijing Normal University, Beijing 100875, China. E-MAIL: ; Xing-ce Wang ; Ping Guo

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

Published in:

2006 International Conference on Machine Learning and Cybernetics

Date of Conference:

13-16 Aug. 2006