Feature-oriented image enhancement using shock filters
SIAM Journal on Numerical Analysis
Restoring Images Degraded by Spatially Variant Blur
SIAM Journal on Scientific Computing
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
ACM SIGGRAPH 2003 Papers
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Two motion-blurred images are better than one
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Image deblurring with blurred/noisy image pairs
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
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
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
Total variation blind deconvolution
IEEE Transactions on Image Processing
Shape from Sharp and Motion-Blurred Image Pair
International Journal of Computer Vision
| Hi-index | 0.00 |
Photographs taken in low-light conditions are often blurry as a result of camera shake, i.e. a motion of the camera while its shutter is open. Most existing deblurring methods model the observed blurry image as the convolution of a sharp image with a uniform blur kernel. However, we show that blur from camera shake is in general mostly due to the 3D rotation of the camera, resulting in a blur that can be significantly non-uniform across the image. We propose a new parametrized geometric model of the blurring process in terms of the rotational motion of the camera during exposure. This model is able to capture non-uniform blur in an image due to camera shake using a single global descriptor, and can be substituted into existing deblurring algorithms with only small modifications. To demonstrate its effectiveness, we apply this model to two deblurring problems; first, the case where a single blurry image is available, for which we examine both an approximate marginalization approach and a maximum a posteriori approach, and second, the case where a sharp but noisy image of the scene is available in addition to the blurry image. We show that our approach makes it possible to model and remove a wider class of blurs than previous approaches, including uniform blur as a special case, and demonstrate its effectiveness with experiments on synthetic and real images.