Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Coded exposure photography: motion deblurring using fluttered shutter
ACM SIGGRAPH 2006 Papers
A Closed Form Solution to Natural Image Matting
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
High-quality motion deblurring from a single image
ACM SIGGRAPH 2008 papers
Editorial: Neurocomputing for vision research
Neurocomputing
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invertible motion blur in video
ACM SIGGRAPH 2009 papers
Semi-blind image restoration using a local neural approach
Neurocomputing
Image quality assessment based on multiscale geometric analysis
IEEE Transactions on Image Processing
No-reference image quality assessment in contourlet domain
Neurocomputing
Visual-context boosting for eye detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
Non-Negative Patch Alignment Framework
IEEE Transactions on Neural Networks
Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent
IEEE Transactions on Image Processing
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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.