Video-based non-uniform object motion blur estimation and deblurring

Motion deblurring is a challenging problem in computer vision. Most previous blind deblurring approaches usually assume that the Point Spread Function (PSF) is spatially invariant. However, non-uniform motions exist ubiquitously and cannot be handled successfully. In this paper, we present an automatic method for object motion deblurring based on non-uniform motion information from video. First, the feature points of the object are tracked throughout a video sequence. Then, the object motion between frames is estimated and the circular blurring paths (i.e. PSFs) of each point are computed along the linear moving path in polar coordinates. Finally, an alpha matte of the blurred object is extracted to separate the foreground from the background, and an iterative Richardson–Lucy algorithm is carried out on the foreground using the obtained blurring paths. Experimental results show our proposed approach outperforms the state-of-the-art motion deblurring algorithms.