Motion blur identification from image gradients Export

@proceedings{citeulike:3181305, abstract = {Restoration of a degraded image from motion blurring is highly dependent on the estimation of the blurring kernel. Most of the existing motion deblurring techniques model the blurring kernel with a shift-invariant box filter, which holds true only if the motion among images is of uniform velocity. In this paper, we present a spectral analysis of image gradients, which leads to a better configuration for identifying the blurring kernel of more general motion types (uniform velocity motion, accelerated motion and vibration). Furthermore, we introduce a hybrid Fourier-Radon transform to estimate the parameters of the blurring kernel with improved robustness to noise over available techniques. The experiments on both simulated images and real images show that our algorithm is capable of accurately identifying the blurring kernel for a wider range of motion types.}, author = {Ji, Hui and Liu, Chaoqiang}, booktitle = {Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on}, citeulike-article-id = {3181305}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/CVPR.2008.4587537}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4587537}, doi = {10.1109/CVPR.2008.4587537}, journal = {Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on}, keywords = {partial}, pages = {1--8}, posted-at = {2008-09-02 11:11:44}, priority = {2}, title = {Motion blur identification from image gradients}, url = {http://dx.doi.org/10.1109/CVPR.2008.4587537}, year = {2008} }

TY - CONF ID - citeulike:3181305 L3 - citeulike-article-id:3181305 N2 - Restoration of a degraded image from motion blurring is highly dependent on the estimation of the blurring kernel. Most of the existing motion deblurring techniques model the blurring kernel with a shift-invariant box filter, which holds true only if the motion among images is of uniform velocity. In this paper, we present a spectral analysis of image gradients, which leads to a better configuration for identifying the blurring kernel of more general motion types (uniform velocity motion, accelerated motion and vibration). Furthermore, we introduce a hybrid Fourier-Radon transform to estimate the parameters of the blurring kernel with improved robustness to noise over available techniques. The experiments on both simulated images and real images show that our algorithm is capable of accurately identifying the blurring kernel for a wider range of motion types. JF - Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on EP - 8 TI - Motion blur identification from image gradients T2 - Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on SP - 1 KW - partial AU - Ji, Hui AU - Liu, Chaoqiang PY - 2008/// UR - http://dx.doi.org/10.1109/CVPR.2008.4587537 ER -