Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Image deblurring with blurred/noisy image pairs
ACM SIGGRAPH 2007 papers
High-quality motion deblurring from a single image
ACM SIGGRAPH 2008 papers
ACM SIGGRAPH Asia 2009 papers
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
Image deblurring using inertial measurement sensors
ACM SIGGRAPH 2010 papers
Two-phase kernel estimation for robust motion deblurring
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Single image deblurring using motion density functions
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Motion blur removal from photographs
Motion blur removal from photographs
Blind deconvolution using a normalized sparsity measure
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Efficient marginal likelihood optimization in blind deconvolution
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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This paper proposes using a mosaic image patches composed of the most informative edges found in the original blurry image for the purpose of estimating a motion blur kernel with minimum computational cost. To select these patches we develop a new image analysis tool to efficiently locate informative patches we call the informative-edge map. The combination of patch mosaic and informative patch selection enables a new motion blur kernel estimation algorithm to recover blur kernels far more quickly and accurately than existing state-of-the-art methods. We also show that patch mosaic can form a framework for reducing the computation time of other motion deblurring algorithms with minimal modification. Experimental results with various test images show that our algorithm to be 5-100 times faster than previously published blind motion deblurring algorithms while achieving equal or better estimation accuracy.