Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/3201

Robust dual motion deblurring

Authors Chen, J.
Yuan, L.
Tang, C.-K.
Quan, L.
Issue Date 2008
Source 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR , 2008
Summary This paper presents a robust algorithm to deblur two consecutively captured blurred photos from camera shaking. Previous dual motion deblurring algorithms succeeded in small and simple motion blur and are very sensitive to noise. We develop a robust feedback algorithm to perform iteratively kernel estimation and image deblurring. In kernel estimation, the stability and capability of the algorithm is greatly improved by incorporating a robust cost function and a set of kernel priors. The robust cost function serves to reject outliers and noise, while kernel priors, including sparseness and continuity, remove ambiguity and maintain kernel shape. In deblurring, we propose a novel and robust approach which takes two blurred images as input to infer the clear image. The deblurred image is then used as feedback to refine kernel estimation. Our method can successfully estimate large and complex motion blurs which cannot be handled by previous dual or single image motion deblurring algorithms. The results are shown to be significantly better than those of previous approaches. ©2008 IEEE.
Subjects
ISSN 1063-6919
ISBN 978-1-4244-2242-5
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Language English
Format Conference paper
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