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 Fig. 1.
• 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.
• Applications:
  • Space-variant motion deblurring with a modified Richardson-Lucy algorithm,
  • Blur/motion synthesis.

Fig. 1: Illustration of the motion blur constraint.

1. 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]
    
2. 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)
    
3. Shengyang 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.

Results:

Space-variant motion estimation

Blur / Motion synthesis

Space-variant motion deblurring

Updated 9/2008. Copyright © 2006-2008 Ying Wu