Shicheng Zheng Li Xu Jiaya Jia
Forward Motion Blurred Image | Blur Removed Image |
Abstract
We handle a special type of motion blur considering that cameras move primarily forward or backward. Solving this type of blur is of unique practical importance since nearly all car, traffic and bike-mounted cameras follow out-ofplane translational motion. We start with the study of geometric models and analyze the difficulty of existing methods to deal with them. We also propose a solution accounting for depth variation. Homographies associated with different 3D planes are considered and solved for in an optimization framework. Our method is verified on several natural image examples that cannot be satisfyingly dealt with by previous methods.
Downloads
"Forward Motion Deblurring" Shicheng Zheng, Li Xu and Jiaya Jia IEEE International Conference on Computer Vision (ICCV), 2013 [Paper (pdf, 950kb)] [Image Data] (Coming Soon) |
Our Other Deblurring Work
High-Quality Iterative Optimization
Large-Kernel Robust Motion Deblurring
Examples
Forward Blur Visualization
Dotted pattern | Forward blur |
Deblurring Results
Blurr Image | [3] | [2] | Ours |
Blurr Image | [1] | [4] | Ours |
Blurr Image | [1] | [5] | Ours |
References
[1] S. Cho and S. Lee. Fast motion deblurring. SIGGRAPH ASIA 2009.
[2] L. Xu and J. Jia. Two-Phase Kernel Estimation for Robust Motion Deblurring. ECCV 2010.
[3] O. Whyte, J. Sivic, A. Zisserman and J. Ponce. Non-uniform Deblurring for Shaken Images. CVPR 2010.
[4] D. Krishnan, T. Tay, and R. Fergus. Blind Deconvolution using a Normalized Sparsity Measure. CVPR 2011.
[5] L. Xu, S. Zheng, and J. Jia. Unnatural L0 Sparse Representation for Natural Image Deblurring. CVPR 2013.