Motion from Blur
Supported in part by NSF and DARPA
Motion blur retains some information about motion, based on which motion may
be recovered from blurred images. This is a difficult problem, as the situations
of motion blur can be quite complicated, such as they may be space variant,
nonlinear, and local. This paper addresses a very challenging problem: can we
recover motion blindly from a single motion-blurred image?
There are mainly three contributions in our work
- Motion blur constraint: a major contribution of this paper is a new
finding of an elegant motion blur constraint. Exhibiting a very similar
mathematical form as the optical flow constraint, this linear constraint
applies locally to pixels in the image. An illustration example is shown in
- Space-variant motion blur estimation: a number of challenging
problems can be addressed under a unified framework, including:
- Motion blur estimation with a global parametric form, such as affine and
rotational motion blur,
- Multiple motion blur patterns estimation and segmentation,
- Nonparametric motion blur field estimation.
- Space-variant motion deblurring with a modified Richardson-Lucy
- Blur/motion synthesis.
Fig. 1: Illustration of the motion blur constraint.
- Shengyang Dai and Ying Wu, "Motion
from Blur", IEEE
Conference on Computer Vision and Pattern Recognition (CVPR'08),
Anchorage, Alaska, USA, June 24-26, 2008. (Oral) [Slides]
- Shengyang Dai
and Ying Wu, "Estimating Space-variant Motion
Blur without Deblurring", IEEE
Conference on Image Processing (ICIP'08),
San Diego, California, USA, October 12-15, 2008. (Oral)
Dai, Ming Yang, Ying Wu, and Aggelos K.Katsaggelos, "Tracking
Motion-blurred Targets in Video", IEEE Conference on Image
Processing (ICIP'06), Atlanta, Georgia, USA, October 8-11, 2006.
Space-variant motion estimation
Blur / Motion synthesis
Space-variant motion deblurring
Updated 9/2008. Copyright ©
2006-2008 Ying Wu