Video deblurring for hand-held cameras using patch-based synthesis
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Registration Based Non-uniform Motion Deblurring
Computer Graphics Forum
Blind correction of optical aberrations
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Bayesian blind deconvolution with general sparse image priors
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Recording and playback of camera shake: benchmarking blind deconvolution with a real-world database
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Face recognition using fast neighborhood component analysis with spatially smooth regularizer
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Single-Image blind deblurring for non-uniform camera-shake blur
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
MRF-Based blind image deconvolution
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Kernel estimation from salient structure for robust motion deblurring
Image Communication
Robust blind motion deblurring using near-infrared flash image
Journal of Visual Communication and Image Representation
Shape from Sharp and Motion-Blurred Image Pair
International Journal of Computer Vision
| Hi-index | 0.00 |
Camera shake leads to non-uniform image blurs. State-of-the-art methods for removing camera shake model the blur as a linear combination of homographically transformed versions of the true image. While this is conceptually interesting, the resulting algorithms are computationally demanding. In this paper we develop a forward model based on the efficient filter flow framework, incorporating the particularities of camera shake, and show how an efficient algorithm for blur removal can be obtained. Comprehensive comparisons on a number of real-world blurry images show that our approach is not only substantially faster, but it also leads to better deblurring results.