Mathematical Programming: Series A and B
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Coded exposure photography: motion deblurring using fluttered shutter
ACM SIGGRAPH 2006 Papers
Image deblurring with blurred/noisy image pairs
ACM SIGGRAPH 2007 papers
Image and depth from a conventional camera with a coded aperture
ACM SIGGRAPH 2007 papers
High-quality motion deblurring from a single image
ACM SIGGRAPH 2008 papers
Progressive inter-scale and intra-scale non-blind image deconvolution
ACM SIGGRAPH 2008 papers
ACM SIGGRAPH Asia 2009 papers
Variational Bayesian blind deconvolution using a total variation prior
IEEE Transactions on Image Processing
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
Correction of Spatially Varying Image and Video Motion Blur Using a Hybrid Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Richardson-Lucy Deblurring for Scenes under a Projective Motion Path
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient marginal likelihood optimization in blind deconvolution
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Semi-blind image restoration via Mumford-Shah regularization
IEEE Transactions on Image Processing
Fast removal of non-uniform camera shake
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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This paper proposes an optimization-based blind image deconvolution method. The proposed method relies on imposing a discrete MRF prior on the deconvolved image. The use of such a prior leads to a very efficient and powerful deconvolution algorithm that carefully combines advanced optimization techniques. We demonstrate the extreme effectiveness of our method by applying it on a wide variety of very challenging cases that involve the inference of large and complicated blur kernels.